Articles de revues : « Ids and devices »

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Naz, Naila, Muazzam A. Khan, Suliman A. Alsuhibany, Muhammad Diyan, Zhiyuan Tan, Muhammad Almas Khan et Jawad Ahmad. « Ensemble learning-based IDS for sensors telemetry data in IoT networks ». Mathematical Biosciences and Engineering 19, n^o 10 (2022) : 10550–80. http://dx.doi.org/10.3934/mbe.2022493.

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<abstract>The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocols such as CoAP, MQTT, DDS, etc. Study shows that these protocols are vulnerable to attack and prove a significant threat to IoT telemetry data. Within a network, IoT devices are interdependent, and the behaviour of one device depends on the data coming from another device. An intruder exploits vulnerabilities of a device's interdependent feature and can alter the telemetry data to indirectly control the behaviour of other dependent devices in a network. Therefore, securing IoT devices have become a significant concern in IoT networks. The research community often proposes intrusion Detection Systems (IDS) using different techniques. One of the most adopted techniques is machine learning (ML) based intrusion detection. This study suggests a stacking-based ensemble model makes IoT devices more intelligent for detecting unusual behaviour in IoT networks. The TON-IoT (2020) dataset is used to assess the effectiveness of the proposed model. The proposed model achieves significant improvements in accuracy and other evaluation measures in binary and multi-class classification scenarios for most of the sensors compared to traditional ML algorithms and other ensemble techniques.</abstract>

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Dat-Thinh, Nguyen, Ho Xuan-Ninh et Le Kim-Hung. « MidSiot : A Multistage Intrusion Detection System for Internet of Things ». Wireless Communications and Mobile Computing 2022 (21 février 2022) : 1–15. http://dx.doi.org/10.1155/2022/9173291.

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Internet of Things (IoT) has been thriving in recent years, playing an important role in a multitude of various domains, including industry 4.0, smart transportation, home automation, and healthcare. As a result, a massive number of IoT devices are deployed to collect data from our surrounding environment and transfer these data to other systems over the Internet. This may lead to cybersecurity threats, such as denial of service attacks, brute-force attacks, and unauthorized accesses. Unfortunately, many IoT devices lack solid security mechanisms and hardware security supports because of their limitations in computational capability. In addition, the heterogeneity of devices in IoT networks causes nontrivial challenges in detecting security threats. In this article, we present a collaborative intrusion detection system (IDS), namely, MidSiot, deployed at both Internet gateways and IoT local gateways. Our proposed IDS consists of three stages: (1) classifying the type of each IoT device in the IoT network; (2) differentiating between benign and malicious network traffic; and (3) identifying the type of attacks targeting IoT devices. The last two stages are handled by the Internet gateways, whereas the first stage is on the local gateway to leverage the computational resources from edge devices. The evaluation results on three popular IDS datasets (IoTID20, CIC-IDS-2017, and BOT-IoT) indicate our proposal could detect seven common cyberattacks targeting IoT devices with an average accuracy of 99.68% and outperforms state-of-the-art IDSs. This demonstrates that MidSiot could be an effective and practical IDS to protect IoT networks.

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Gluskin, Efim. « APS Insertion Devices : Recent Developments and Results ». Journal of Synchrotron Radiation 5, n^o 3 (1 mai 1998) : 189–95. http://dx.doi.org/10.1107/s0909049597013769.

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The Advanced Photon Source (APS) now has a total of 23 insertion devices (IDs). Over two-thirds of them are installed on the storage ring. The installed devices include 18, 27 and 55 mm-period undulators; an 85 mm-period wiggler; a 16 cm-period elliptical multipole wiggler; and many 33 mm-period undulators. Most of the IDs occupy storage-ring straight sections equipped with 8 mm vertical-aperture vacuum chambers. All of the IDs were measured magnetically at the APS and, in most cases, underwent a final magnetic tuning in order to minimize variation in the various integrals of the field through the ID over the full gap range. Special shimming techniques to correct magnetic field parameters in appropriate gap-dependent ways were developed and applied. Measurements of the closed-orbit distortion as a function of the ID gap variation have been completed, and results are in a good agreement with magnetic measurements. Spectral diagnostics of the ID radiation, including measurements of the absolute spectral flux, brilliance and polarization, show excellent agreement between calculated and measured results. Studies of the sensitivity of IDs to radiation exposure and measurements of the dose rate received by the IDs are in progress.

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Matebesi, Unopa, et Nonofo M. J. Ditshego. « Indium Gallium Zinc Oxide FinFET Compared with Silicon FinFET ». Journal of Nano Research 68 (29 juin 2021) : 103–13. http://dx.doi.org/10.4028/www.scientific.net/jnanor.68.103.

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Indium gallium zinc oxide fin-field effect transistor (IGZO FinFET) characteristics are investigated and then compared with Zinc oxide fin-field effect transistor (ZnO FinFET) and the Silicon fin-field effect transistor (Si FinFET). This was done using 3D simulation. The threshold voltage for Si, ZnO, and IGZO is 0.75 V, 0.30 V and 0.05 V respectively. The silicon device has the highest transconductance (5.0 x 10-7 S) and performs better than the other devices because it has less fixed charge defects. IGZO has the second-best value of Gm (3.6 x 10-7 S), ZnO has the least value of Gm (3.4 x 10-7 S). Si device has the least drain current (IDS) value of 2.0 x 10-7 A, ZnO device has a better IDS value of 6.2 x 10-6 A while IGZO device has the best IDS value of 1.6 x 10-5 A. IGZO is better than Si by two (2) order magnitude. The field effect mobility is 50.0 cm2/Vs for all three devices.

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Levichev, Eugene, et Nikolay Vinokurov. « Undulators and Other Insertion Devices ». Reviews of Accelerator Science and Technology 03, n^o 01 (janvier 2010) : 203–20. http://dx.doi.org/10.1142/s1793626810000403.

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This article reviews insertion devices (IDs) — wigglers and undulators — both for synchrotron radiation production and for machine parameter control. As there are many types of wigglers and undulators and it is impossible to describe them all in detail, here we particularly emphasize the design, characteristics and tolerances of undulators for free electron lasers and damping wigglers for controlling beam damping parameters. The influence of periodic IDs on beam parameters in cyclic accelerators is described in brief. A profound description of different ID types can be found in [1].

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Cortese, Yvonne J., Victoria E. Wagner, Morgan Tierney, Declan Devine et Andrew Fogarty. « Review of Catheter-Associated Urinary Tract Infections and In Vitro Urinary Tract Models ». Journal of Healthcare Engineering 2018 (14 octobre 2018) : 1–16. http://dx.doi.org/10.1155/2018/2986742.

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Catheter-associated urinary tract infections (CAUTIs) are one of the most common nosocomial infections and can lead to numerous medical complications from the mild catheter encrustation and bladder stones to the severe septicaemia, endotoxic shock, and pyelonephritis. Catheters are one of the most commonly used medical devices in the world and can be characterised as either indwelling (ID) or intermittent catheters (IC). The primary challenges in the use of IDs are biofilm formation and encrustation. ICs are increasingly seen as a solution to the complications caused by IDs as ICs pose no risk of biofilm formation due to their short time in the body and a lower risk of bladder stone formation. Research on IDs has focused on the use of antimicrobial and antibiofilm compounds, while research on ICs has focused on preventing bacteria entering the urinary tract or coming into contact with the catheter. There is an urgent need for in vitro urinary tract models to facilitate faster research and development for CAUTI prevention. There are currently three urinary tract models that test IDs; however, there is only a single very limited model for testing ICs. There is currently no standardised urinary tract model to test the efficacies of ICs.

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R. Zarzoor, Ahmed, Nadia Adnan Shiltagh Al-Jamali et Dina A. Abdul Qader. « Intrusion detection method for internet of things based on the spiking neural network and decision tree method ». International Journal of Electrical and Computer Engineering (IJECE) 13, n^o 2 (1 avril 2023) : 2278. http://dx.doi.org/10.11591/ijece.v13i2.pp2278-2288.

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The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices’ power usage. Also, a rand order code (ROC) technique is used with SNN to detect cyber-attacks. The proposed method is evaluated by comparing its performance with two other methods: IDS-DNN and IDS-SNNTLF by using three performance metrics: detection accuracy, latency, and energy usage. The simulation results have shown that IDS-SNNDT attained low power usage and less latency in comparison with IDS-DNN and IDS-SNNTLF methods. Also, IDS-SNNDT has achieved high detection accuracy for cyber-attacks in contrast with IDS-SNNTLF.

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Kimbrough, Joevonte, Lauren Williams, Qunying Yuan et Zhigang Xiao. « Dielectrophoresis-Based Positioning of Carbon Nanotubes for Wafer-Scale Fabrication of Carbon Nanotube Devices ». Micromachines 12, n^o 1 (25 décembre 2020) : 12. http://dx.doi.org/10.3390/mi12010012.

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In this paper, we report the wafer-scale fabrication of carbon nanotube field-effect transistors (CNTFETs) with the dielectrophoresis (DEP) method. Semiconducting carbon nanotubes (CNTs) were positioned as the active channel material in the fabrication of carbon nanotube field-effect transistors (CNTFETs) with dielectrophoresis (DEP). The drain-source current (IDS) was measured as a function of the drain-source voltage (VDS) and gate-source voltage (VGS) from each CNTFET on the fabricated wafer. The IDS on/off ratio was derived for each CNTFET. It was found that 87% of the fabricated CNTFETs was functional, and that among the functional CNTFETs, 30% of the CNTFETs had an IDS on/off ratio larger than 20 while 70% of the CNTFETs had an IDS on/off ratio lower than 20. The highest IDS on/off ratio was about 490. The DEP-based positioning of carbon nanotubes is simple and effective, and the DEP-based device fabrication steps are compatible with Si technology processes and could lead to the wafer-scale fabrication of CNT electronic devices.

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Alsharif, Maram, et Danda B. Rawat. « Study of Machine Learning for Cloud Assisted IoT Security as a Service ». Sensors 21, n^o 4 (3 février 2021) : 1034. http://dx.doi.org/10.3390/s21041034.

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Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research focuses on the functionality metrics of ML techniques, depicting their prediction effectiveness, but overlooked their operational requirements. ML techniques are resource-demanding that require careful adaptation to fit the limited computing resources of a large sector of their operational platform, namely, embedded systems. In this paper, we propose cloud-based service architecture for managing ML models that best fit different IoT device operational configurations for security. An IoT device may benefit from such a service by offloading to the cloud heavy-weight activities such as feature selection, model building, training, and validation, thus reducing its IDS maintenance workload at the IoT device and get the security model back from the cloud as a service.

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Javed, Abbas, Amna Ehtsham, Muhammad Jawad, Muhammad Naeem Awais, Ayyaz-ul-Haq Qureshi et Hadi Larijani. « Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes ». Future Internet 16, n^o 6 (5 juin 2024) : 200. http://dx.doi.org/10.3390/fi16060200.

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Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.

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Myridakis, Dimitrios, Georgios Spathoulas, Athanasios Kakarountas et Dimitrios Schinianakis. « Smart Devices Security Enhancement via Power Supply Monitoring ». Future Internet 12, n^o 3 (10 mars 2020) : 48. http://dx.doi.org/10.3390/fi12030048.

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The continuous growth of the number of Internet of Things (IoT) devices and their inclusion to public and private infrastructures has introduced new applciations to the market and our day-to-day life. At the same time, these devices create a potential threat to personal and public security. This may be easily understood either due to the sensitivity of the collected data, or by our dependability to the devices’ operation. Considering that most IoT devices are of low cost and are used for various tasks, such as monitoring people or controlling indoor environmental conditions, the security factor should be enhanced. This paper presents the exploitation of side-channel attack technique for protecting low-cost smart devices in an intuitive way. The work aims to extend the dataset provided to an Intrusion Detection Systems (IDS) in order to achieve a higher accuracy in anomaly detection. Thus, along with typical data provided to an IDS, such as network traffic, transmitted packets, CPU usage, etc., it is proposed to include information regarding the device’s physical state and behaviour such as its power consumption, the supply current, the emitted heat, etc. Awareness of the typical operation of a smart device in terms of operation and functionality may prove valuable, since any deviation may warn of an operational or functional anomaly. In this paper, the deviation (either increase or decrease) of the supply current is exploited for this reason. This work aimed to affect the intrusion detection process of IoT and proposes for consideration new inputs of interest with a collateral interest of study. In parallel, malfunction of the device is also detected, extending this work’s application to issues of reliability and maintainability. The results present 100% attack detection and this is the first time that a low-cost security solution suitable for every type of target devices is presented.

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Alsharif, Nada Abdu, Shailendra Mishra et Mohammed Alshehri. « IDS in IoT using Machine ‎Learning and Blockchain ». Engineering, Technology & ; Applied Science Research 13, n^o 4 (9 août 2023) : 11197–203. http://dx.doi.org/10.48084/etasr.5992.

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The rise of IoT devices has brought forth an urgent need for enhanced security and privacy measures, as IoT devices are vulnerable to cyber-attacks that compromise the security and privacy of users. Traditional security measures do not provide adequate protection for such devices. This study aimed to investigate the use of machine learning and blockchain to improve the security and privacy of IoT devices, creating an intrusion detection system powered by machine learning algorithms and using blockchain to encrypt interactions between IoT devices. The performance of the whole system and different machine learning algorithms was evaluated on an IoT network using simulated attack data, achieving a detection accuracy of 99.9% when using Random Forrest, demonstrating its effectiveness in detecting attacks on IoT networks. Furthermore, this study showed that blockchain technology could improve security and privacy by providing a tamper-proof decentralized communication system.

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Rani, K. Swapna, Gayatri Parasa, D. Hemanand, S. V. Devika, S. Balambigai, M. I. Thariq Hussan, Koppuravuri Gurnadha Gupta, Y. J. Nagendra Kumar et Alok Jain. « Implementation of a multi-stage intrusion detection systems framework for strengthening security on the internet of things ». MATEC Web of Conferences 392 (2024) : 01106. http://dx.doi.org/10.1051/matecconf/202439201106.

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The Internet of Things (IoT) expansion has introduced a new era of interconnectedness and creativity inside households. Various independent gadgets are now controlled from a distance, enhancing efficiency and organization. This results in increased security risks. Competing vendors rapidly develop and release novel connected devices, often paying attention to security concerns. As a result, there is a growing number of assaults against smart gadgets, posing risks to users' privacy and physical safety. The many technologies used in IoT complicate efforts to provide security measures for smart devices. Most intrusion detection methods created for such platforms rely on monitoring network activities. On multiple platforms, intrusions are challenging to detect accurately and consistently via network traces. This research provides a Multi-Stage Intrusion Detection System (MS-IDS) for intrusion detection that operates on the host level. The study employs personal space and kernel space data and Machine Learning (ML) methods to identify different types of intrusions in electronic devices. The proposed MS-IDS utilizes tracing methods that automatically record device activity, convert this data into numerical arrays to train multiple ML methods, and trigger warnings upon detecting an incursion. The research used several ML methods to enhance the ability to see with little impact on the monitoring devices. The study evaluated the MS-IDS approach in a practical home automation system under genuine security risks.

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Kaushik, Sunil, Akashdeep Bhardwaj, Abdullah Alomari, Salil Bharany, Amjad Alsirhani et Mohammed Mujib Alshahrani. « Efficient, Lightweight Cyber Intrusion Detection System for IoT Ecosystems Using MI2G Algorithm ». Computers 11, n^o 10 (20 septembre 2022) : 142. http://dx.doi.org/10.3390/computers11100142.

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The increase in internet connectivity has led to an increased usage of the Internet of Things (IoT) and devices on the internet. These IoT devices are becoming the backbone of Industry 4.0. The dependence on IoT devices has made them vulnerable to cyber-attacks. IoT devices are often deployed in harsh conditions, challenged with less computational costs, and starved with energy. All these limitations make it tough to deploy accurate intrusion detection systems (IDSs) in IoT devices and make the critical IoT ecosystem more susceptible to cyber-attacks. A new lightweight IDS and a novel feature selection algorithm are introduced in this paper to overcome the challenges of computational cost and accuracy. The proposed algorithm is based on the Information Theory models to select the feature with high statistical dependence and entropy reduction in the dataset. This feature selection algorithm also showed an increase in performance parameters and a reduction in training time of 27–63% with different classifiers. The proposed IDS with the algorithm showed accuracy, Precision, Recall, and F1-Score of more than 99% when tested with the CICIDS2018 dataset. The proposed IDS is competitive in accuracy, Precision, Recall, and training time compared to the latest published research. The proposed IDS showed consistent performance on the UNSWNB15 dataset.

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Otoum, Safa, Burak Kantarci et Hussein Mouftah. « A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures ». ACM Transactions on Internet Technology 21, n^o 4 (22 juillet 2021) : 1–22. http://dx.doi.org/10.1145/3406093.

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Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

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Liu, Xinzhong, Shouzhi Xuan, Shunqiang Tian, Xu Wu, Yihao Gong, Liyuan Tan et Guangwei Jiao. « Orbit stabilization for the new insertion devices in SSRF ». Journal of Instrumentation 19, n^o 01 (1 janvier 2024) : T01003. http://dx.doi.org/10.1088/1748-0221/19/01/t01003.

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Abstract During the Shanghai Synchrotron Radiation Facility (SSRF) beamline project, fourteen new insertion devices (IDs) have been successfully installed and transitioned to operation. However, these new IDs led to considerable closed orbit distortions in the SSRF storage ring. In the canted beamlines, the theoretical current values of dipoles required for the closed dual-canted angle were established, however, errors in the actual machine make a significant effect on closed orbit. To address this, an adjustment configuration, based on the response matrix, was developed for the closed dual-canted angles. Additionally, an orbit feedforward compensation system equipped with corrector coils, was employed. As a result, the maximum horizontal and vertical orbit distortions induced by the IDs were successfully decreased to less than 5.0 μm and 3.5 μm, respectively. Furthermore, a specialized local fast feedback system was designed to maintain the precise orbit and angle stability for the Hard X-ray Nanoprobe beamline.

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Chavanne, Joel, Pascal Elleaume et Pierre Van Vaerenbergh. « The ESRF Insertion Devices ». Journal of Synchrotron Radiation 5, n^o 3 (1 mai 1998) : 196–201. http://dx.doi.org/10.1107/s0909049597012855.

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The European Synchrotron Radiation facility is presently operating 47 segments of insertion devices (IDs). A record brilliance of 1 × 1020 photons s−1 (0.1% bandwidth)−1 mm−2 mrad−2 has been reached. Almost all devices are built with permanent magnets with or without iron pole pieces. They have been mechanically and magnetically designed and field-measured in house. Multipole shimming has been applied to all devices to remove the integrated dipole and higher-order multipole fields, thereby reducing the interaction between the IDs and the stored beam. For all undulators, the field errors have been corrected further using spectrum shimming in order to achieve ideal spectral brilliance on all harmonic numbers from 1 to 15. A significant effort has been made to optimize the magnet terminations for both field-integral correction and phasing. A phasing scheme of the undulator segments has been developed which allows the independent manufacture and operation of individual segments. Several designs for undulator phasing are presented, together with a comparison between hybrid and pure-permanent-magnet technology. A new type of variable-polarization helical undulator is presented.

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Adefemi Alimi, Kuburat Oyeranti, Khmaies Ouahada, Adnan M. Abu-Mahfouz, Suvendi Rimer et Oyeniyi Akeem Alimi. « Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things ». Journal of Sensor and Actuator Networks 11, n^o 3 (1 juillet 2022) : 32. http://dx.doi.org/10.3390/jsan11030032.

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The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.

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Najam, Faraz, et Yun Seop Yu. « Compact Trap-Assisted-Tunneling Model for Line Tunneling Field-Effect-Transistor Devices ». Applied Sciences 10, n^o 13 (28 juin 2020) : 4475. http://dx.doi.org/10.3390/app10134475.

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Trap-assisted-tunneling (TAT) is a well-documented source of severe subthreshold degradation in tunneling field-effect-transistors (TFET). However, the literature lacks in numerical or compact TAT models applied to TFET devices. This work presents a compact formulation of the Schenk TAT model that is used to fit experimental drain-source current (Ids) versus gate-source voltage (Vgs) data of an L-shaped and line tunneling type TFET. The Schenk model incorporates material-dependent fundamental physical constants that play an important role in influencing the TAT generation (GTAT) including the lattice relaxation energy, Huang–Rhys factor, and the electro-optical frequency. This makes fitting any experimental data using the Schenk model physically relevant. The compact formulation of the Schenk TAT model involved solving the potential profile in the TFET and using that potential profile to calculate GTAT using the standard Schenk model. The GTAT was then approximated by the Gaussian distribution function for compact implementation. The model was compared against technology computer-aided design (TCAD) results and was found in reasonable agreement. The model was also used to fit an experimental device’s Ids–Vgs characteristics. The results, while not exactly fitting the experimental data, follow the general experimental Ids–Vgs trend reasonably well; the subthreshold slope was loosely similar to the experimental device. Additionally, the ON-current, especially to make a high drain-source bias model accurate, can be further improved by including effects such as electrostatic degradation and series resistance.

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Catillo, Marta, Antonio Pecchia et Umberto Villano. « A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection ». Applied Sciences 13, n^o 2 (7 janvier 2023) : 837. http://dx.doi.org/10.3390/app13020837.

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Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability.

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Umar, Umar, Kamaluddeen Usman .., Mohd Fadzil Hassan, Aminu Aminu Muazu et M. S. Liew. « An IoT Device-Level Vulnerability Control Model Through Federated Detection ». Journal of Intelligent Systems and Internet of Things 12, n^o 2 (2024) : 89–98. http://dx.doi.org/10.54216/jisiot.120207.

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In the rapidly expanding Internet of Things (IoT) landscape, the security of IoT devices is a major concern. The challenge lies in the lack of intrusion detection systems (IDS) models for these devices. This is due to resource limitations, resulting in, single point of failure, delayed threat detection and privacy issues when centralizing IDS processing. To address this, a LiteDLVC model is proposed in this paper, employing a multi-layer perceptron (MLP) in a federated learning (FL) approach to minimize the vulnerabilities in IoT system. This model manages smaller datasets from individual devices, reducing processing time and optimizing computing resources. Importantly, in the event of an attack, the LiteDLVC model targets only the compromised device, protecting the FL aggregator and other IoT devices. The model's evaluation using the BoT-IoT dataset on TensorFlow Federated (TFF) demonstrates higher accuracy and better performance with full features subset of 99.99% accuracy on test set and achieved average of 1.11sec in detecting bot attacks through federated detection. While on 10-best subset achieved 99.99 on test with 1.14sec as average detection time. Notably, this highlights that LiteDLVC model can potential secure IoT device from device level very efficiently. To improve the global model convergence, we are currently exploring the use genetic algorithm. This could lead to better performance on diverse IoT data distributions, and increased overall efficiency in FL scenes with non-IID data.

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Romeo, M. D. Shakhawat Shafaet. « Intrusion Detection System (IDS) in Internet of Things (IoT) Devices for Smart Home ». International Journal of Psychosocial Rehabilitation 23, n^o 4 (20 décembre 2019) : 1217–27. http://dx.doi.org/10.37200/ijpr/v23i4/pr190448.

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Isong, Bassey, Otshepeng Kgote et Adnan Abu-Mahfouz. « Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems ». Electronics 13, n^o 12 (17 juin 2024) : 2370. http://dx.doi.org/10.3390/electronics13122370.

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The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security.

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Alsulami, Rehab, Batoul Alqarni, Rawan Alshomrani, Fatimah Mashat et Tahani Gazdar. « IoT Protocol-Enabled IDS based on Machine Learning ». Engineering, Technology & ; Applied Science Research 13, n^o 6 (5 décembre 2023) : 12373–80. http://dx.doi.org/10.48084/etasr.6421.

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During the last decade, Internet of Things (IoT) devices have become widely used in smart homes, smart cities, factories, and many other areas to facilitate daily activities. As IoT devices are vulnerable to many attacks, especially if they are not frequently updated, Intrusion Detection Systems (IDSs) must be used to defend them. Many existing IDSs focus on specific types of IoT application layer protocols, such as MQTT, CoAP, and HTTP. Additionally, many existing IDSs based on machine learning are inefficient in detecting attacks in IoT applications because they use non-IoT-dedicated datasets. Therefore, there is no comprehensive IDS that can detect intrusions that specifically target IoT devices and their various application layer protocols. This paper proposes a new comprehensive IDS for IoT applications called IP-IDS, which can equivalently detect MQTT, HTTP, and CoAP-directed intrusions with high accuracy. Three different datasets were used to train the model: Bot-IoT, MQTT-IoT-IDS2020, and CoAP-DDoS. The obtained results showed that the proposed model outperformed the existing models trained on the same datasets. Additionally, the proposed DT and LSTM models reached an accuracy of 99.9%.

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Chen, Chii-Wen, Mu-Chun Wang, Cheng-Hsun-Tony Chang, Wei-Lun Chu, Shun-Ping Sung et Wen-How Lan. « Hot Carrier Stress Sensing Bulk Current for 28 nm Stacked High-k nMOSFETs ». Electronics 9, n^o 12 (8 décembre 2020) : 2095. http://dx.doi.org/10.3390/electronics9122095.

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This work primarily focuses on the degradation degree of bulk current (IB) for 28 nm stacked high-k (HK) n-channel metal–oxide–semiconductor field-effect transistors (MOSFETs), sensed and stressed with the channel-hot-carrier test and the drain-avalanche-hot-carrier test, and uses a lifetime model to extract the lifetime of the tested devices. The results show that when IB reaches its maximum, the ratio of VGS/VDS values at this point, in the meanwhile, gradually increases in the tested devices from the long-channel to the short ones, not just located at one-third to one half. The possible ratiocination is due to the ON-current (IDS), in which the short-channel devices provide larger IDS impacting the drain junction and generating more hole carriers at the surface channel near the drain site. In addition, the decrease in IB after hot-carrier stress is not only the increment in threshold voltage VT inducing the decrease in IDS, but also the increment in the recombination rate due to the mechanism of diffusion current. Ultimately, the device lifetime uses Berkley’s model to extract the slope parameter m of the lifetime model. Previous studies have reported m-values ranging from 2.9 to 3.3, but in this case, approximately 1.1. This possibly means that the critical energy of the generated interface state becomes smaller, as is the barrier height of the HK dielectric to the conventional silicon dioxide as the gate oxide.

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Hsu, Che-Wei, Yueh-Chin Lin, Ming-Wen Lee et Edward-Yi Chang. « Investigation of the Effect of Different SiNx Thicknesses on the Characteristics of AlGaN/GaN High-Electron-Mobility Transistors in Ka-Band ». Electronics 12, n^o 20 (19 octobre 2023) : 4336. http://dx.doi.org/10.3390/electronics12204336.

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The effect of different SiNx thicknesses on the performance of AlGaN/GaN high-electron-mobility transistors (HEMTs) was investigated in this paper. The current, transconductance (Gm), cut-off frequency (fT), maximum oscillation frequency (fmax), power performance, and output third-order intercept point (OIP3) of devices with three different SiNx thicknesses (150 nm, 200 nm, and 250 nm) were measured and analyzed. The DC measurements revealed an increase in both the drain-source current (IDS) and Gm values of the device with increasing SiNx thickness. The S-parameter measurement results show that devices with a higher SiNx thickness exhibit improved fT and fmax. Regarding power performance, thicker SiNx devices also improve the output power density (Pout) and power-added efficiency (PAE) in the Ka-band. In addition, the two-tone measurement results at 28 GHz show that the OIP3 increased from 35.60 dBm to 40.87 dBm as the SiNx thickness increased from 150 nm to 250 nm. The device’s characteristics improved by appropriately increasing the SiNx thickness.

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Yas, Harith, et Manal M. Nasir. « Securing the IoT : An Efficient Intrusion Detection System Using Convolutional Network ». Journal of Cybersecurity and Information Management 1, n^o 1 (2020) : 30–37. http://dx.doi.org/10.54216/jcim.010105.

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The Internet of Things (IoT) is an ever-expanding network of interconnected devices that enables various applications, such as smart homes, smart cities, and industrial automation. However, with the proliferation of IoT devices, security risks have increased significantly, making it necessary to develop effective intrusion detection systems (IDS) for IoT networks. In this paper, we propose an efficient IDS for complex IoT environments based on convolutional neural networks (CNNs). Our approach uses IoT traffics as input to our CNN architecture to capture representational knowledge required to discriminate different forms of attacks. Our system achieves high accuracy and low false positive rates, even in the presence of complex and dynamic network traffic patterns. We evaluate the performance of our system using public datasets and compare it with other cutting-edge IDS approaches. Our results show that the proposed system outperforms the other approaches in terms of accuracy and false positive rates. The proposed IDS can enhance the security of IoT networks and protect them against various types of cyber-attacks.

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Karamollaoğlu, Hamdullah, İbrahim Alper Doğru et İbrahim Yücedağ. « An Efficient Deep Learningbased Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques ». Information Technology and Control 53, n^o 1 (22 mars 2024) : 243–61. http://dx.doi.org/10.5755/j01.itc.53.1.34933.

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With the increasing use of Internet of Things (IoT) technologies, cyber-attacks on IoT devices are also increasing day by day. Detecting attacks on IoT networks before they cause any damage is crucial for ensuring the security of the devices on these networks. In this study, a novel Intrusion Detection System (IDS) was developed for IoT networks. The IoTID20 and BoT-IoT datasets were utilized during the training phase and performance testing of the proposed IDS. A hybrid method combining the Principal Component Analysis (PCA) and the Bat Optimization (BAT) algorithm was proposed for dimensionality reduction on the datasets. The Synthetic Minority Over-SamplingTechnique (SMOTE) was used to address the problem of data imbalance in the classes of the datasets. The Convolutional Neural Networks (CNN) model, a deep learning method, was employed for attack classification. The proposed IDS achieved an accuracy rate of 99.97% for the IoTID20 dataset and 99.98% for the BoT-IoT dataset in attack classification. Furthermore, detailed analyses were conducted to determine the effects of the dimensionality reduction and data balancing models on the classification performance of the proposed IDS.

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Al-Dhuhli, Maha Abullah, Ammar Khamis Al-Mizaini, Miysaa Salim Al-Braiki et Rajesh Natarajan. « Intrusion detection system to advance IoT security environment ». International Journal of Information Technology, Research and Applications 2, n^o 2 (22 juin 2023) : 10–17. http://dx.doi.org/10.59461/ijitra.v2i2.48.

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Nowadays, with the technological improvement, communications with the things become easier. It helps people to live an easier life, live and work smarter as well as take back control of their lives completely. this smart communication is done in an environment that called Internet of Things (IoT) environment. The Internet of Thing is multiple physical objects that communicate using the internet, allowing sending, and receiving of data. Since it’s a data so it's prone to attack in a goal of steal it, change it and so many reasons. In addition, nowadays hackers are everywhere with so many types. So, it needs to protect those data and the devices, if Internet of Things devices doesn't have enough security to protect the system from being compromised, then many threats and attacks will occur. If the administrator does not apply strong security and develop a plan for system and device prevention, the Internet of Things environment will be weak, which will make the system and devices prone to attack. Unauthorized access will be prevented if the login system includes a signature matching system. This research aims to analyze the traffic security and analyze threats and risks using IoT devices from intruders by applying an IDS to the IoT environment. when the attacker will try to enter to the traffic or send any packets to any IoT devices, the intrusion detection system will send an alert to the administrator that there's something wrong need to check, the IDS will detect the attacker's name, type and from which device he tries to enter to the system, it will analyze the traffic system, as well as will prevent the devices and data from threats.

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Zhang, Yang, Yu Tang, Chaoyang Li, Hua Zhang et Haseeb Ahmad. « Post-Quantum Secure Identity-Based Signature Scheme with Lattice Assumption for Internet of Things Networks ». Sensors 24, n^o 13 (27 juin 2024) : 4188. http://dx.doi.org/10.3390/s24134188.

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The Internet of Things (IoT) plays an essential role in people’s daily lives, such as healthcare, home, traffic, industry, and so on. With the increase in IoT devices, there emerge many security issues of data loss, privacy leakage, and information temper in IoT network applications. Even with the development of quantum computing, most current information systems are weak to quantum attacks with traditional cryptographic algorithms. This paper first establishes a general security model for these IoT network applications, which comprises the blockchain and a post-quantum secure identity-based signature (PQ-IDS) scheme. This model divides these IoT networks into three layers: perceptual, network, and application, which can protect data security and user privacy in the whole data-sharing process. The proposed PQ-IDS scheme is based on lattice cryptography. Bimodal Gaussian distribution and the discrete Gaussian sample algorithm are applied to construct the fundamental difficulty problem of lattice assumption. This assumption can help resist the quantum attack for information exchange among IoT devices. Meanwhile, the signature mechanism with IoT devices’ identity can guarantee non-repudiation of information signatures. Then, the security proof shows that the proposed PQ-IDS can obtain the security properties of unforgeability, non-repudiation, and non-transferability. The efficiency comparisons and performance evaluations show that the proposed PQ-IDS has good efficiency and practice in IoT network applications.

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Khan, Zeeshan Ali, et Ubaid Abbasi. « Reputation Management Using Honeypots for Intrusion Detection in the Internet of Things ». Electronics 9, n^o 3 (29 février 2020) : 415. http://dx.doi.org/10.3390/electronics9030415.

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Internet of Things (IoT) networks consist of tiny devices with limited processing resources and restricted energy budget. These devices are connected to the world-wide web (www) using networking protocols. Considering their resource limitations, they are vulnerable to security attacks by numerous entities on the Internet. The classical security solutions cannot be directly implemented on top of these devices for this reason. However, an Intrusion Detection System (IDS) is a classical way to protect these devices by using low-cost solutions. IDS monitors the network by introducing various metrics, and potential intruders are identified, which are quarantined by the firewall. One such metric is reputation management, which monitors the behavior of the IoT networks. However, this technique may still result in detection error that can be optimized by combining this solution with honeypots. Therefore, our aim is to add some honeypots in the network by distributing them homogeneously as well as randomly. These honeypots will team up with possible maliciously behaving nodes and will monitor their behavior. As per the simulation results, this technique reduces the error rate within the existing IDS for the IoT; however, it costs some extra energy. This trade-off between energy consumption and detection accuracy is studied by considering standard routing and MAC protocol for the IoT network.

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Sahu, Dipti Prava, Biswajit Tripathy et Leena Samantaray. « Optimized Intrusion Detection System in Fog Computing Environment Using Automatic Termination-based Whale Optimization with ELM ». International Journal of Computer Network and Information Security 16, n^o 2 (8 avril 2024) : 79–91. http://dx.doi.org/10.5815/ijcnis.2024.02.07.

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In fog computing, computing resources are deployed at the network edge, which can include routers, switches, gateways, and even end-user devices. Fog computing focuses on running computations and storing data directly on or near the fog devices themselves. The data processing occurs locally on the device, reducing the reliance on network connectivity and allowing for faster response times. However, the conventional intrusion detection system (IDS) failed to provide security during the data transfer between fog nodes to cloud, fog data centres. So, this work implemented the optimized IDS in fog computing environment (OIDS-FCE) using advanced naturally inspired optimization algorithms with extreme learning. Initially, the data preprocessing operation maintains the uniform characteristics in the dataset by normalizing the columns. Then, comprehensive learning particle swarm based effective seeker optimization (CLPS-ESO) algorithm extracts the intrusion specific features by analyzing the internal patterns of all rows, columns. In addition, automatic termination-based whale optimization algorithm (ATWOA) selects the best intrusion features from CLPS-ESO resultant features using correlation analysis. Finally, the hybrid extreme learning machine (HELM) classifies the varies instruction types from ATWOA optimal features. The simulation results show that the proposed OIDS-FCE achieved 98.52% accuracy, 96.38% precision, 95.50% of recall, and 95.90% of F1-score using UNSW-NB dataset, which are higher than other artificial intelligence IDS models.

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Idrissi, Idriss, Mohammed Boukabous, Mostafa Azizi, Omar Moussaoui et Hakim El Fadili. « Toward a deep learning-based intrusion detection system for IoT against botnet attacks ». IAES International Journal of Artificial Intelligence (IJ-AI) 10, n^o 1 (1 mars 2021) : 110. http://dx.doi.org/10.11591/ijai.v10.i1.pp110-120.

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The massive network traffic data between connected devices in the internet of things have taken a big challenge to many traditional intrusion detection systems (IDS) to find probable security breaches. However, security attacks lean towards unpredictability. There are numerous difficulties to build up adaptable and powerful IDS for IoT in order to avoid false alerts and ensure a high recognition precision against attacks, especially with the rising of Botnet attacks. These attacks can even make harmless devices becoming zombies that send malicious traffic and disturb the network. In this paper, we propose a new IDS solution, baptized BotIDS, based on deep learning convolutional neural networks (CNN). The main interest of this work is to design, implement and test our IDS against some well-known Botnet attacks using a specific Bot-IoT dataset. Compared to other deep learning techniques, such as simple RNN, LSTM and GRU, the obtained results of our BotIDS are promising with 99.94% in validation accuracy, 0.58% in validation loss, and the prediction execution time is less than 0.34 ms.

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Maidamwar, Priya R., Prasad P. Lokulwar et Kailash Kumar. « Ensemble Learning Approach for Classification of Network Intrusion Detection in IoT Environment ». International Journal of Computer Network and Information Security 15, n^o 3 (8 juin 2013) : 30–36. http://dx.doi.org/10.5815/ijcnis.2023.03.03.

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Over the last two years,the number of cyberattacks has grown significantly, paralleling the emergence of new attack types as intruder’s skill sets have improved. It is possible to attack other devices on a botnet and launch a man-in-the-middle attack with an IOT device that is present in the home network. As time passes, an ever-increasing number of devices are added to a network. Such devices will be destroyed completely if one or both of them are disconnected from a network. Detection of intrusions in a network becomes more difficult because of this. In most cases, manual detection and intervention is ineffective or impossible. Consequently, it's vital that numerous types of network threats can be better identified with less computational complexity and time spent on processing. Numerous studies have already taken place, and specific attacks are being examined. In order to quickly detect an attack, an IDS uses a well-trained classification model. In this study, multi-layer perceptron classifier along with random forest is used to examine the accuracy, precision, recall and f-score of IDS. IoT environment-based intrusion related benchmark datasets UNSWNB-15 and N_BaIoT are utilized in the experiment. Both of these datasets are relatively newer than other datasets, which represents the latest attack. Additionally, ensembles of different tree sizes and grid search algorithms are employed to determine the best classifier learning parameters. The research experiment's outcomes demonstrate the effectiveness of the IDS model using random forest over the multi-layer perceptron neural network model since it outperforms comparable ensembles analyzed in the literature in terms of K-fold cross validation techniques.

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Meliboev, Azizjon. « IOT NETWORK INTRUSION DETECTION SYSTEM USING MACHINE LEARNING TECHNIQUES ». QO‘QON UNIVERSITETI XABARNOMASI 11 (30 juin 2024) : 112–15. http://dx.doi.org/10.54613/ku.v11i11.972.

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The proliferation of Internet of Things (IoT) devices has transformed various industries by providing smart and automated solutions. However, the extensive connectivity and diverse nature of IoT devices have also introduced significant security challenges, particularly in terms of network intrusion. This paper explores the development and implementation of an Intrusion Detection System (IDS) for IoT networks using Machine learning techniques. The proposed IDS aims to detect and mitigate various cyber threats by analyzing network traffic and identifying anomalous patterns indicative of intrusions. This research contributes to the field of IoT security by providing a robust and scalable intrusion detection solution that leverages the power of machine learning.

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Alsamiri, Jadil, et Khalid Alsubhi. « Federated Learning for Intrusion Detection Systems in Internet of Vehicles : A General Taxonomy, Applications, and Future Directions ». Future Internet 15, n^o 12 (14 décembre 2023) : 403. http://dx.doi.org/10.3390/fi15120403.

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In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

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Khan, Ajmal, Adnan Munir, Zeeshan Kaleem, Farman Ullah, Muhammad Bilal, Lewis Nkenyereye, Shahen Shah, Long D. Nguyen, S. M. Riazul Islam et Kyung-Sup Kwak. « RDSP : Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management ». Sensors 20, n^o 2 (19 janvier 2020) : 548. http://dx.doi.org/10.3390/s20020548.

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In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim’s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.

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Nazir, Anjum, Zulfiqar Memon, Touseef Sadiq, Hameedur Rahman et Inam Ullah Khan. « A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection ». Sensors 23, n^o 19 (28 septembre 2023) : 8153. http://dx.doi.org/10.3390/s23198153.

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The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.

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Zegarra Rodríguez, Demóstenes, Ogobuchi Daniel Okey, Siti Sarah Maidin, Ekikere Umoren Udo et João Henrique Kleinschmidt. « Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection ». PLOS ONE 18, n^o 10 (16 octobre 2023) : e0286652. http://dx.doi.org/10.1371/journal.pone.0286652.

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Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks resulting from malicious network flows during data exchange between the connected devices. Various security threats have shown high adverse effects on the availability, functionality, and usability of the devices among which denial of service (DoS) and distributed denial of service (DDoS), which attempt to exhaust the capacity of the IoT network (gateway), thereby causing failure in the functionality of the system have been more pronounced. Various machine learning and deep learning algorithms have been used to propose intelligent intrusion detection systems (IDS) to mitigate the challenging effects of these network threats. One concern is that although deep learning algorithms have shown good accuracy results on tabular data, not all deep learning algorithms can perform well on tabular datasets, which happen to be the most commonly available format of datasets for machine learning tasks. Again, there is also the challenge of model explainability and feature selection, which affect model performance. In this regard, we propose a model for IDS that uses attentive mechanisms to automatically select salient features from a dataset to train the IDS model and provide explainable results, the TabNet-IDS. We implement the proposed model using the TabNet algorithm based on PyTorch which is a deep-learning framework. The results obtained show that the TabNet architecture can be used on tabular datasets for IoT security to achieve good results comparable to those of neural networks, reaching an accuracy of 97% on CIC-IDS2017, 95% on CSE-CICIDS2018 and 98% on CIC-DDoS2019 datasets.

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Titov, D. N. « Detection of intrusions into the Internet of things system ». Interexpo GEO-Siberia 8, n^o 2 (18 mai 2022) : 118–25. http://dx.doi.org/10.33764/2618-981x-2022-8-2-118-125.

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The Internet of Things (IoT) is an evolving technology in which computing devices and sensors exchange data over a network to solve various tasks and provide services. Medical care, remote control of devices, interaction between machines, etc. are services provided today for users without human intervention. Despite a number of advantages, this technology also has disadvantages, one of which is security. There are many methods used to protect the Internet of Things, one of them is the Intrusion detection System (IDS) - this is one of the most original and well-organized methods that can protect Internet of Things devices from intruders and detect their attack with high accuracy. The article discusses such types of attacks as DDoS/DoS, hello flood and Sybil attack, etc., as well as various types of approaches to IDS, such as machine learning, SDN and machine-based identifiers, which can be useful for preventing and detecting attacks on Internet of Things devices.

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Elangovan, Surya, Stone Cheng et Edward Yi Chang. « Reliability Characterization of Gallium Nitride MIS-HEMT Based Cascode Devices for Power Electronic Applications ». Energies 13, n^o 10 (21 mai 2020) : 2628. http://dx.doi.org/10.3390/en13102628.

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We present a detailed study of dynamic switching instability and static reliability of a Gallium Nitride (GaN) Metal-Insulator-Semiconductor High-Electron-Mobility-Transistor (MIS-HEMT) based cascode switch under off-state (negative bias) Gate bias stress (VGS, OFF). We have investigated drain channel current (IDS, Max) collapse/degradation and turn-on and rise-time (tR) delay, on-state resistance (RDS-ON) and maximum transconductance (Gm, max) degradation and threshold voltage (VTH) shift for pulsed and prolonged off-state gate bias stress VGS, OFF. We have found that as stress voltage magnitude and stress duration increases, similarly IDS, Max and RDS-ON degradation, VTH shift and turn-on/rise time (tR) delay, and Gm, max degradation increases. In a pulsed off-state VGS, OFF stress experiment, the device instabilities and degradation with electron trapping effects are studied through two regimes of stress voltages. Under low stress, VTH shift, IDS collapse, RDS-ON degradation has very minimal changes, which is a result of a recoverable surface state trapping effect. For high-stress voltages, there is an increased and permanent VTH shift and high IDS, Max and RDS-ON degradation in pulsed VGS, Stress and increased rise-time and turn-on delay. In addition to this, a positive VTH shift and Gm, max degradation were observed in prolonged stress experiments for selected high-stress voltages, which is consistent with interface state generation. These findings provide a path to understand the failure mechanisms under room temperature and also to accelerate the developments of emerging GaN cascode technologies.

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Idrissi, Idriss, Mostafa Azizi et Omar Moussaoui. « An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices ». Indonesian Journal of Electrical Engineering and Computer Science 25, n^o 2 (1 février 2022) : 1140. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1140-1150.

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Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.

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Park, Sohyun, et Suphee Sun. « "A Study on The Direction of Sleep-Tech Devices for Middle-aged Women :Focusing on preferences for device types and function" ». Journal of Industrial Design Studies 14, n^o 4 (31 décembre 2020) : 51–60. http://dx.doi.org/10.37254/ids.2020.12.54.05.51.

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Mahmod, Md Jubayer al, et Ujjwal Guin. « A Robust, Low-Cost and Secure Authentication Scheme for IoT Applications ». Cryptography 4, n^o 1 (8 mars 2020) : 8. http://dx.doi.org/10.3390/cryptography4010008.

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The edge devices connected to the Internet of Things (IoT) infrastructures are increasingly susceptible to piracy. These pirated edge devices pose a serious threat to security, as an adversary can get access to the private network through these non-authentic devices. It is necessary to authenticate an edge device over an unsecured channel to safeguard the network from being infiltrated through these fake devices. The implementation of security features demands extensive computational power and a large hardware/software overhead, both of which are difficult to satisfy because of inherent resource limitation in the IoT edge devices. This paper presents a low-cost authentication protocol for IoT edge devices that exploits power-up states of built-in SRAM for device fingerprint generations. Unclonable ID generated from the on-chip SRAM could be unreliable, and to circumvent this issue, we propose a novel ID matching scheme that alleviates the need for enhancing the reliability of the IDs generated from on-chip SRAMs. Security and different attack analysis show that the probability of impersonating an edge device by an adversary is insignificant. The protocol is implemented using a commercial microcontroller, which requires a small code overhead. However, no modification of device hardware is necessary.

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Pamungkas, I. Gede Agung Krisna, Tohari Ahmad et Royyana Muslim Ijtihadie. « Analysis of Autoencoder Compression Performance in Intrusion Detection System ». International Journal of Safety and Security Engineering 12, n^o 3 (30 juin 2022) : 395–401. http://dx.doi.org/10.18280/ijsse.120314.

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Exchanging data between devices is getting easier and faster just by using a network. Nevertheless, many factors threaten this process and the network itself. Implementing an Intrusion Detection System (IDS) may minimize the risk since it can identify and prevent attacks on the network. There are many methods to design an IDS to work optimally only by reducing data dimensions, one of which is by using the Autoencoder. However, its data dimensions may not have been optimal, which affects the IDS performance. In this study, we work on this problem. This study shows that one of the dimensional reduction methods can get optimal results. It indicates that it is implementable to secure the network.

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Long, S. B., Zhi Gang Li, X. W. Zhao, Bao Qin Chen et Ming Liu. « Coulomb Staircases and Differential Conductance Oscillations in a SIMOX-Based Single-Electron Transistor ». Solid State Phenomena 121-123 (mars 2007) : 513–16. http://dx.doi.org/10.4028/www.scientific.net/ssp.121-123.513.

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For compatibility with present CMOS devices, the single-electron transistor (SET) is preferably made in silicon. In this paper, a Si-based SET with in-plane side gates is proposed, which is fabricated in a SIMOX (Separation by IMplanted OXygen) wafer using electron beam lithography (EBL) with high-resolution SAL601 negative e-beam resist and inductively coupled plasma (ICP) etching. Carefully controlled the process, the SET with a 70-nm-radius Coulomb island is successfully fabricated. The Rds-T characteristics of the SET indicate that the device has typical semiconductor characteristics and the co-tunneling phenomena is impossible to occur. The Ids-Vds characteristics of the SET at different values of Vg (-10 V, 0 V, 10 V) measured at the temperature of 2 K all show Coulomb staircases. And the good reproducibility of the Ids-Vds characteristics can also be realized. The corresponding dIds/dVds-Vds characteristics show the clear differential conductance oscillations at 2 K. The Ids-Vg curve at Vds = 0.1 V and Vg = 10 V approximately exhibits Coulomb oscillations. The fabrication process is quite easy and this kind of Si-based SET has the advantages of simplicity, IC-orientation and compatibility with traditional CMOS process.

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Ngo, Duc-Minh, Dominic Lightbody, Andriy Temko, Cuong Pham-Quoc, Ngoc-Thinh Tran, Colin C. Murphy et Emanuel Popovici. « HH-NIDS : Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security ». Future Internet 15, n^o 1 (26 décembre 2022) : 9. http://dx.doi.org/10.3390/fi15010009.

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This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks’ security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation’s ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc7z020-1clg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000EVKIT.

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Musleh, Dhiaa, Meera Alotaibi, Fahd Alhaidari, Atta Rahman et Rami M. Mohammad. « Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT ». Journal of Sensor and Actuator Networks 12, n^o 2 (29 mars 2023) : 29. http://dx.doi.org/10.3390/jsan12020029.

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With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy. Therefore, this research aims to introduce a study on ML-based IDS in IoT, considering different feature extraction algorithms with several ML models. This study evaluated several feature extractors, including image filters and transfer learning models, such as VGG-16 and DenseNet. Additionally, several machine learning algorithms, including random forest, K-nearest neighbors, SVM, and different stacked models were assessed considering all the explored feature extraction algorithms. The study presented a detailed evaluation of all combined models using the IEEE Dataport dataset. Results showed that VGG-16 combined with stacking resulted in the highest accuracy of 98.3%.

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Jo, Wooyeon, Sungjin Kim, Changhoon Lee et Taeshik Shon. « Packet Preprocessing in CNN-Based Network Intrusion Detection System ». Electronics 9, n^o 7 (16 juillet 2020) : 1151. http://dx.doi.org/10.3390/electronics9071151.

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The proliferation of various connected platforms, including Internet of things, industrial control systems (ICSs), connected cars, and in-vehicle networks, has resulted in the simultaneous use of multiple protocols and devices. Chaotic situations caused by the usage of different protocols and various types of devices, such as heterogeneous networks, implemented differently by vendors renders the adoption of a flexible security solution difficult, such as recent deep learning-based intrusion detection system (IDS) studies. These studies optimized the deep learning model for their environment to improve performance, but the basic principle of the deep learning model used was not changed, so this can be called a next-generation IDS with a model that has little or no requirements. Some studies proposed IDS based on unsupervised learning technology that does not require labeled data. However, not using available assets, such as network packet data, is a waste of resources. If the security solution considers the role and importance of the devices constituting the network and the security area of the protocol standard by experts, the assets can be well used, but it will no longer be flexible. Most deep learning model-based IDS studies used recurrent neural network (RNN), which is a supervised learning model, because the characteristics of the RNN model, especially when the long-short term memory (LSTM) is incorporated, are better configured to reflect the flow of the packet data stream over time, and thus perform better than other supervised learning models such as convolutional neural network (CNN). However, if the input data induce the CNN’s kernel to sufficiently reflect the network characteristics through proper preprocessing, it could perform better than other deep learning models in the network IDS. Hence, we propose the first preprocessing method, called “direct”, for network IDS that can use the characteristics of the kernel by using the minimum protocol information, field size, and offset. In addition to direct, we propose two more preprocessing techniques called “weighted” and “compressed”. Each requires additional network information; therefore, direct conversion was compared with related studies. Including direct, the proposed preprocessing methods are based on field-to-pixel philosophy, which can reflect the advantages of CNN by extracting the convolutional features of each pixel. Direct is the most intuitive method of applying field-to-pixel conversion to reflect an image’s convolutional characteristics in the CNN. Weighted and compressed are conversion methods used to evaluate the direct method. Consequently, the IDS constructed using a CNN with the proposed direct preprocessing method demonstrated meaningful performance in the NSL-KDD dataset.

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Naithani, Kanchan. « AI-based Intrusion Detection System for Internet of Things (IoT) Networks ». Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, n^o 2 (10 septembre 2019) : 1095–100. http://dx.doi.org/10.17762/turcomat.v10i2.13631.

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The rise of the Internet of Things has brought about various advantages, such as providing us with more efficient and effortless activities. Unfortunately, the lack of security solutions has also led to the development of new threats. One of these is the exploitation of vulnerabilities in the networks of IoT devices. In order to effectively address the security threats that can arise in the networks of IoT devices, there needs to be an effective intrusion detection system (IDS). In the field of security, the use of artificial intelligence (AI) powered IDS has shown promising promise. Through deep learning and machine learning techniques, these systems can learn and adapt quickly to new threats. This paper presents an evaluation of the performance of an AI-based security system on a large dataset. The research begins with a literature review of the previous studies related to the security of IoT devices and intrusion detection. We then develop a methodology that includes the data collected for evaluation and training, an AI model architecture for intrusion detection, and the evaluation metrics. The paper presents the results of the study and discusses the performance of the AI-based IDS compared to the existing solutions for addressing security threats in Internet of Things networks. It also explores the potential of this technology for future research. The findings of this study contribute to the growing body of research on the security of IoT networks and intrusion detection. It shows that an AI-based IDS can perform better than the existing solutions in identifying and mitigating threats. The study's findings show the potential of deep learning and machine learning techniques to enhance the security of IoT networks. It also highlights the scope of this technology's application in other security domains.

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