Journal articles on the topic 'Signal processing for network security'
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Alapati, Yaswanth Kumar, and Suban Ravichandran. "An Efficient Signal Processing Model for Malicious Signal Identification and Energy Consumption Reduction for Improving Data Transmission Rate." Traitement du Signal 38, no. 3 (June 30, 2021): 837–43. http://dx.doi.org/10.18280/ts.380330.
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One of the fields which needs the most security is Ad hoc Network (ANET). The term ANET guarantees that there is no central authority so as to administer the signals. Security is a basic issue while using ANET for establishing communication. A ANET is an assortment of remote signals that can progressively be set up at anyplace and whenever without utilizing any prior system framework. Because of its volatile nature, it has mobility issues to improve the arrangement of the system. One of the difficulties is to recognize the malicious signals in the system. Because of malicious signals, data loss or high energy consumption will occur which reduce the overall performance of the ANET. There are a few circumstances when at least one signal in the system become malevolent and will destroy the limit of the system. The point of this work is to recognize the malignant signals quickly to avoid loss of data. The conventional strategy for firewall and encryption isn't adequate to secure the system. In this way a malicious signal identification framework must be added to the ad hoc network. A signal needs to be secured when utilizing the resources and to provide secure communication. The ad hoc networks have several issues like, congestion, overload, data loss and energy consumption. In the proposed work a framework for Rapid Malicious Signal Detection with Energy Consumption Reduction (RMSDwECR) Method is proposed. The proposed method is compared with the traditional methods in terms of load in the network, data loss ratio, signal transmission rate, energy consumption levels, malicious signal identification time and throughput levels. The proposed method exhibits better performance than the traditional methods.
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Gao, Feng, Yun Wu, and Shang Qiong Lu. "LabVIEW-Based Virtual Laboratory for Digital Signal Processing." Advanced Materials Research 268-270 (July 2011): 2150–57. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.2150.
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Based on National Instruments LabVIEW 2009, a network-edition virtual laboratory for digital signal processing (DSP) has been developed. Which is composed of three functional modules, that is, virtual experiment table, information management, and network communication. Hereinto, virtual experiment table is composed of two sub-modules, i.e. resource & document and simulation experiment; information management module is composed of four sub-modules, i.e. database, user registration, security verification and system management; network communication module is implemented by LabVIEW Web Server. The DSP Virtual Laboratory is suit for experimental teaching of a range of subjects, such as Digital Signal Processing, Signals & Systems, etc. And the designed virtual laboratory can provide users with a remote virtual experimental platform without time and space constraints.
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Xue, Lian, and Cheng-song Hu. "A Vibration Signal Processing of Large-scale Structural Systems Based on Wireless Sensor." International Journal of Online Engineering (iJOE) 13, no. 05 (May 14, 2017): 43. http://dx.doi.org/10.3991/ijoe.v13i05.7050.
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The inherent characteristics of large-scale structural system are also called modal parameters, which include natural frequency, damping ratio and vibration mode. They are the basis for analyzing dynamic characteristics of large-scale structural system. Modal Parameter Identification is a modern method, and it is used to identify the vibration signals. At present, the problem of large-scale structural system security is paid more and more attention to, so the method of modal parameter recognition is very significant. A fast integral method is put forward to eliminate the trend item of vibration signals, and the vibration signals are collected through the wireless sensor network (acceleration signal), so as to obtain the integrated vibration signal (speed and displacement signal). The polynomial fitting method is applied to eliminate the trend items in the sampling integral, and improve the operation speed and accuracy by the relationship among the various coefficients. Then, they are discretized to meet the wireless sensor network requirements of "online" processing and analysis. Through the simulation of acceleration signals based on finite element modeling and the processing of actual acquisition acceleration signals based on wireless sensor network, the effectiveness of this method was verified. As a result, the precision effect by sampling frequency and the data length is summarized
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Cheng, Jie, Bingjie Lin, Jiahui Wei, and Ang Xia. "The Compound Prediction Analysis of Information Network Security Situation based on Support Vector Combined with BP Neural Network Learning Algorithm." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 489–96. http://dx.doi.org/10.46300/9106.2022.16.60.
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In order to solve the problem of low security of data in network transmission and inaccurate prediction of future security situation, an improved neural network learning algorithm is proposed in this paper. The algorithm makes up for the shortcomings of the standard neural network learning algorithm, eliminates the redundant data by vector support, and realizes the effective clustering of information data. In addition, the improved neural network learning algorithm uses the order of data to optimize the "end" data in the standard neural network learning algorithm, so as to improve the accuracy and computational efficiency of network security situation prediction.MATLAB simulation results show that the data processing capacity of support vector combined BP neural network is consistent with the actual security situation data requirements, the consistency can reach 98%. the consistency of the security situation results can reach 99%, the composite prediction time of the whole security situation is less than 25s, the line segment slope change can reach 2.3% ,and the slope change range can reach 1.2%,, which is better than BP neural network algorithm.
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Xiang, Zhongwu, Weiwei Yang, Gaofeng Pan, Yueming Cai, and Yi Song. "Physical Layer Security in Cognitive Radio Inspired NOMA Network." IEEE Journal of Selected Topics in Signal Processing 13, no. 3 (June 2019): 700–714. http://dx.doi.org/10.1109/jstsp.2019.2902103.
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Demidov, R. A., P. D. Zegzhda, and M. O. Kalinin. "Threat Analysis of Cyber Security in Wireless Adhoc Networks Using Hybrid Neural Network Model." Automatic Control and Computer Sciences 52, no. 8 (December 2018): 971–76. http://dx.doi.org/10.3103/s0146411618080084.
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Ji, Cheongmin, Taehyoung Ko, and Manpyo Hong. "CA-CRE: Classification Algorithm-Based Controller Area Network Payload Format Reverse-Engineering Method." Electronics 10, no. 19 (October 8, 2021): 2442. http://dx.doi.org/10.3390/electronics10192442.
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In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control units (TCUs). This leads to an attack vector in which attackers can control vehicles remotely once they gain access to in-vehicle networks (IVNs) and can discover the formats of important messages. Although the format information is kept secret by car manufacturers, CAN is vulnerable, since payloads are transmitted in plain text. In contrast, the secrecy of message formats inhibits IVN security research by third-party researchers. It also hinders effective security tests for in-vehicle networks as performed by evaluation authorities. To mitigate this problem, a method of reverse-engineering CAN payload formats is proposed. The method utilizes classification algorithms to predict signal boundaries from CAN payloads. Several features were uniquely chosen and devised to quantify the type-specific characteristics of signals. The method is evaluated on real-world and synthetic CAN traces, and the results show that our method can predict at least 10% more signal boundaries than the existing methods.
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Tu, Jun, Willies Ogola, Dehong Xu, and Wei Xie. "Intrusion Detection Based on Generative Adversarial Network of Reinforcement Learning Strategy for Wireless Sensor Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 478–82. http://dx.doi.org/10.46300/9106.2022.16.58.
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Due to the wireless nature of wireless sensor networks (WSN), the network can be deployed in most of the unattended environment, which makes the networks more vulnerable for attackers who may listen to the traffic and inject their own nodes in the sensor network. In our work, we research on a novel machine learning algorithm on intrusion detection based on reinforcement learning (RL) strategy using generative adversarial network (GAN) for WSN which can automatically detect intrusion or malicious attacks into the network. We combine Actor-Critic Algorithm in RL with GAN in a simulated WSN. The GAN is employed as part of RL environment to generate fake data with possible attacks, which is similar to the real data generated by the sensor networks. Its main aim is to confuse the adversarial network into differentiating between the real and fake data with possible attacks. The results that is from the experiments are based on environment of GAN and Network Simulator 3 (NS3) illustrate that Actor-Critic&GAN algorithm enhances security of the simulated WSN by protecting the networks data against adversaries and improves on the accuracy of the detection.
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Peng, Qi Hua. "An Improved Abnormal Behavior Feature Detection Algorithm of Network Information Based on Fractional Fourier Transform." Applied Mechanics and Materials 513-517 (February 2014): 2408–11. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2408.
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The feature extraction and detection problem of network information abnormal behavior was researched in this paper. The network attack tended to ambiguity in hidden, and the abnormal behavior of network information is referred as a data signal series, and it was existed in the network information space with strong interference. Traditional detection method was hard to detect the abnormal signal. On the basis of fractional Fourier transform (FRFT), an improved abnormal behavior feature detection algorithm of network information was proposed. The properties such as energy gathering and noise suppression of 4-order cumulant slice were taken in advantage. In the post processing of fractional Fourier transform detection, the post processing operator of 4 order cumulant was introduced in the detection algorithm, the post energy was gathered in the fractional Fourier domain, the signal accumulation was likely to be increased, and the interference noise could be restrained effectively. Simulation results show that the improved algorithm has perfect noise suppression performance, and it can detect and extract the abnormal behavior feature in the network space to maximum. The detection performance is improved greatly, and the research result can be applied in the network information warfare and network security areas.
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Ahmed, Aseel K., and Abbas Akram Khorsheed. "Open network structure and smart network to sharing cybersecurity within the 5G network." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (July 1, 2022): 573. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp573-582.
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The <span>next-generation communication system incorporates information technology (IT) and operations technology (OT) for generating, delivering, and collecting, and obtaining communication power. We plan to include a brief outline of internet of thing (IoT) communication and its context, along with security concerns that arise for IoT data on the network and some methods for detecting and avoiding cyber security threats. With the rise of the 5G networks, we introduce the smart network's emergent technology and its opportunities and more cybersecurity issues. Whereas, finding or responding to a power outage is an essential part of system security That is why we will discuss the innumerable advantages of 5G networks and we must also cover the inevitable problems that we will encounter in power delivery. The use of smart IoT communication technologies is becoming more common in the energy sector, particularly with the network (5G. The smart network and energy flow integration Real-time data on generation, electricity distribution, and energy consumption is measured using computers and cutting-edge technologies. This information aids utility companies in managing electricity supply and demand, as well as price. While enhanced communication and information technologies are unquestionably crucial to </span>the smart network.
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Lin, Bingjie, Jie Cheng, Jiahui Wei, and Ang Xia. "A Sensing Method of Network Security Situation Based on Markov Game Model." International Journal of Circuits, Systems and Signal Processing 16 (January 14, 2022): 531–36. http://dx.doi.org/10.46300/9106.2022.16.66.
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The sensing of network security situation (NSS) has become a hot issue. This paper first describes the basic principle of Markov model and then the necessary and sufficient conditions for the application of Markov game model. And finally, taking fuzzy comprehensive evaluation model as the theoretical basis, this paper analyzes the application fields of the sensing method of NSS with Markov game model from the aspects of network randomness, non-cooperative and dynamic evolution. Evaluation results show that the sensing method of NSS with Markov game model is best for financial field, followed by educational field. In addition, the model can also be used in the applicability evaluation of the sensing methods of different industries’ network security situation. Certainly, in different categories, and under the premise of different sensing methods of network security situation, the proportions of various influencing factors are different, and once the proportion is unreasonable, it will cause false calculation process and thus affect the results.
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Lee, Sanguk, Incheol Jeong, and Woo-Geun Ahn. "Study on Search and Rescue System for Military and Civil use by using COSPAS SARSAT and Terrestrial Communication Network." E3S Web of Conferences 94 (2019): 01018. http://dx.doi.org/10.1051/e3sconf/20199401018.
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We propose search and rescue system for the both military and civil uses with minimizing burden to COSPAS SARSAT by using terrestrial communication network like LTE as an example in the paper. The terrestrial network may not be limited to LTE but extended to 5G, SigFox, LoRa, etc, later on. For security, special encryption mechanism is applied to the both COSPAS SARSAT and Terrestrial links. Also, operation concept for critical and normal situation. Implementation result and enhancing positioning accuracy by using rescue signal itself from ground signal processing is presented too.
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Behlilovic, Narves. "One Approach to Improving Smart Environment Communication via the Security Parameter." International Journal of Embedded and Real-Time Communication Systems 13, no. 1 (January 1, 2022): 1–30. http://dx.doi.org/10.4018/ijertcs.313042.
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Improving smart environment communication remains a final unachievable destination. Continuous optimization in smart environment communication is mandatory because of an emerging number of connected devices. Carefully observing its parameters and demands leads to acknowledging existing challenges and boundaries regarding areas covered with signal and possibilities of approaching network architecture, limited battery resources in certain nodes of network architecture, privacy, and security of existing data transfer. One approach to dealing with these communication challenges and boundaries is focusing on important technical parameters respectively, signal processing speed, communication nodes distance, and communication channel security. The aim of this article is to point out these most important communication parameters in smart environments and how changing those can affect communication. Its original contribution is represented in establishing principles for governing security parameters by using permanent magnets in order to produce Faraday's rotation and thus manipulate the whole process of communication in a smart environment.
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Tefera, Mulugeta Kassaw, Zengwang Jin, and Shengbing Zhang. "A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security." Sensors 22, no. 9 (May 9, 2022): 3589. http://dx.doi.org/10.3390/s22093589.
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With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods. With the objective of advancing future PLS research, this review paper presents an elaborate discussion on the design and optimization approaches of wireless PLS techniques. In particular, we focus on both signal processing and information-theoretic security approaches to investigate the optimization techniques and system designs of PLS strategies. The review begins with the fundamental concepts that are associated with PLS, including a discussion on conventional cryptographic techniques and wiretap channel models. We then move on to discuss the performance metrics and basic optimization schemes that are typically adopted in PLS design strategies. The research directions for secure system designs and optimization problems are then reviewed in terms of signal processing, resource allocation and node/antenna selection. Thereafter, the applications of AI and ML technologies in the optimization and design of PLS systems are discussed. In this context, the ML- and AI-based solutions that pertain to end-to-end physical layer joint optimization, secure resource allocation and signal processing methods are presented. We finally conclude with discussions on future trends and technical challenges that are related to the topics of PLS system design and the benefits of AI technologies.
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He, Jialuan, Zirui Xing, Tianqi Xiang, Xin Zhang, Yinghai Zhou, Chuanyu Xi, and Hai Lu. "Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring." Sensors 21, no. 17 (August 24, 2021): 5688. http://dx.doi.org/10.3390/s21175688.
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In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.
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Wichary, Tomasz, Jordi Mongay Batalla, Constandinos X. Mavromoustakis, Jerzy Żurek, and George Mastorakis. "Network Slicing Security Controls and Assurance for Verticals." Electronics 11, no. 2 (January 11, 2022): 222. http://dx.doi.org/10.3390/electronics11020222.
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This paper focuses on the security challenges of network slice implementation in 5G networks. We propose that network slice controllers support security by enabling security controls at different network layers. The slice controller orchestrates multilevel domains with resources at a very high level but needs to understand how to define the resources at lower levels. In this context, the main outstanding security challenge is the compromise of several resources in the presence of an attack due to weak resource isolation at different levels. We analysed the current standards and trends directed to mitigate the vulnerabilities mentioned above, and we propose security controls and classify them by efficiency and applicability (easiness to develop). Security controls are a common way to secure networks, but they enforce security policies only in respective areas. Therefore, the security domains allow for structuring the orchestration principles by considering the necessary security controls to be applied. This approach is common for both vendor-neutral and vendor-dependent security solutions. In our classification, we considered the controls in the following fields: (i) fair resource allocation with dynamic security assurance, (ii) isolation in a multilayer architecture and (iii) response to DDoS attacks without service and security degradation.
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Wang, Huan, Jian Gu, Jianping Zhao, Dan Liu, Xin Sui, Xiaoqiang Di, and Bo Li. "Prediction Method of Network Security Situation Based on GA-LSSVM Time Series Analysis." Advances in Modelling and Analysis B 60, no. 2 (June 30, 2017): 372–90. http://dx.doi.org/10.18280/ama_b.600208.
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Lazaar, Saiida. "Contribution of wavelets to cybersecurity: Intrusion detection systems using neural networks." General Letters in Mathematics 10, no. 2 (June 2021): 24–30. http://dx.doi.org/10.31559/glm2021.10.2.2.
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The gigantic growth of the exchanged digital data has raised important security challenges. In this ecosystem, connected objects, systems and networks are exposed to various cyber threats endangering sensitive data and compromising confidentiality, integrity and authentication. Modelling intrusion detection systems (IDS) constitute an important research field with a major goal to protect targeted systems and networks against malicious activities. Many network IDS have been recently designed with artificial intelligence techniques. Signal processing techniques have been applied in network detection systems due to their ability to help for a good intrusion detection. At the same context, the wavelet transform which is considered as a very efficient tool for the decomposition and reconstruction of signals can be recommended in the design of powerful network detection systems, and can be applied for data preprocessing denoising and extracting information. Wavelets combined to neural networks can be useful for modelling intrusion detection with the main challenges to reduce the false alarms, increase the test accuracy and increase novel attacks detection rate. In this work, we present a major contribution in the research field to better understand how wavelets and neural networks can be combined for modelling efficient IDS.
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Lafia, Diafale, Mistura Laide Sanni, Rasheed Ayodeji Adetona, Bodunde Odunola Akinyemi, and Ganiyu Adesola Aderounmu. "Signal Processing-based Model for Primary User Emulation Attacks Detection in Cognitive Radio Networks." Journal of Computing and Information Technology 29, no. 2 (July 4, 2022): 77–88. http://dx.doi.org/10.20532/cit.2021.1005297.
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Cognitive Radio Networks (CRNs) have been conceived to improve the efficiency of accessing the spectrum. However, these networks are prone to various kinds of attacks and failures that can compromise the security and performance of their users. One of the notable malicious attacks in cognitive radio networks is the Primary User Emulation (PUE) attack, which results in underutilization and unavailability of the spectrum and low operational efficiency of the network. This study developed an improved technique for detecting PUE attacks in cognitive radio networks and further addressed the characteristics of sparsely populated cognitive radio networks and the mobility of the primary users. A hybrid signal processing-based model was developed using the free space path loss and additive Gaussian noise models. The free space path loss model was used to detect the position of the transmitter, while the additive Gaussian noise model was used to analyze the signal transmitted, i.e., energy detection in the spectrum at the detected location. The proposed model was benchmarked with an existing model using the number of secondary users and the velocity of the transmitter as performance parameters. The simulation results show that the proposed model has improved accuracy in detecting primary user emulation attacks. It was concluded that the proposed hybrid model with respect to the number of secondary users and the velocity of the transmitter can be used for primary user emulation attack detection in cognitive radio networks.
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Kalinin, M. O., and A. A. Minin. "Security evaluation of a wireless ad-hoc network with dynamic topology." Automatic Control and Computer Sciences 51, no. 8 (December 2017): 899–901. http://dx.doi.org/10.3103/s0146411617080119.
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Du, Zhiqiang, Haitao Yao, Yanfang Fu, Zijian Cao, Hongtao Liang, and Jinkang Ren. "Network Situation Assessment Method Based on Improved BP Neural Network." Electronics 12, no. 3 (January 17, 2023): 483. http://dx.doi.org/10.3390/electronics12030483.
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Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.
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Khonde, Shraddha R., and Venugopal Ulagamuthalvi. "Hybrid Architecture for Distributed Intrusion Detection System Using Semi-supervised Classifiers in Ensemble Approach." Advances in Modelling and Analysis B 63, no. 1-4 (December 31, 2020): 10–19. http://dx.doi.org/10.18280/ama_b.631-403.
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Security of data is becoming a big treat today because of modern attacks. All the data passing through network is at risk as intruders can easily access and modify data. Security to the network is provided using Intrusion Detection System (IDS) which helps to monitor and analyze each packet entering or passing through the network. In this paper hybrid architecture for IDS is proposed which can work as an intelligent system in distributed environment. Proposed system makes use of semi-supervised machine learning classifiers into an ensemble approach. Classifiers used are Support vector machine, decision tree and k-nearest neighbor. Ensemble of this classifier is done and final prediction is given by majority voting algorithm. This system makes use of feature selection technique to reduce number of features used for training various classifiers. Experiments are conducted on NSL-KDD dataset. From results it is observed that ensemble technique increases accuracy by 3% and reduces false alarm rate by 0.05. System performance improves if used in ensemble approach as compare to individual classifier.
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Heo, Jeonghwan, and Jechang Jeong. "Deceptive Techniques to Hide a Compressed Video Stream for Information Security." Sensors 21, no. 21 (October 29, 2021): 7200. http://dx.doi.org/10.3390/s21217200.
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With the recent development of video compression methods, video transmission on traditional devices and video distribution using networks has increased in various devices such as drones, IP cameras, and small IoT devices. As a result, the demand for encryption techniques such as MPEG-DASH for transmitting streams over networks is increasing. These video stream security methods guarantee stream confidentiality. However, they do not hide the fact that the encrypted stream is being transmitted over the network. Considering that sniffing attacks can analyze the entropy of the stream and scan huge amounts of traffic on the network, to solve this problem, the deception method is required, which appears unencrypted but a confidential stream. In this paper, we propose the new deception method that utilizes standard NAL unit rules of video codec, where the unpromised device shows the cover video and the promised device shows the secret video for deceptive security. This method allows a low encryption cost and the stream to dodge entropy-based sniffing scan attacks. The proposed stream shows that successful decoding using five standard decoders and processing performance was 61% faster than the conventional encryption method in the test signal conformance set. In addition, a network encrypted stream scan method the HEDGE showed classification results that our stream is similar to a compressed video.
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Song, Wenzhan, Fangyu Li, Maria Valero, and Liang Zhao. "Toward Creating a Subsurface Camera." Sensors 19, no. 2 (January 14, 2019): 301. http://dx.doi.org/10.3390/s19020301.
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In this article, the framework and architecture of a Subsurface Camera (SAMERA) are envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals and computes a 3D subsurface image in situ in real time. The basic mechanism is geophysical waves propagating/reflected/refracted through subsurface enter a network of geophysical sensors, where a 2D or 3D image is computed and recorded; control software may be connected to this network to allow view of the 2D/3D image and adjustment of settings such as resolution, filter, regularization, and other algorithm parameters. System prototypes based on seismic imaging have been designed. SAMERA technology is envisioned as a game changer to transform many subsurface survey and monitoring applications, including oil/gas exploration and production, subsurface infrastructures and homeland security, wastewater and CO2 sequestration, and earthquake and volcano hazard monitoring. System prototypes for seismic imaging have been built. Creating SAMERA requires interdisciplinary collaboration and the transformation of sensor networks, signal processing, distributed computing, and geophysical imaging.
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Olanrewaju, Rashidah Funke, Burhan Ul Islam Khan, Miss Laiha Binti Mat Kiah, Nor Aniza Abdullah, and Khang Wen Goh. "Decentralized Blockchain Network for Resisting Side-Channel Attacks in Mobility-Based IoT." Electronics 11, no. 23 (December 1, 2022): 3982. http://dx.doi.org/10.3390/electronics11233982.
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The inclusion of mobility-based Internet-of-Things (IoT) devices accelerates the data transmission process, thereby catering to IoT users’ demands; however, securing the data transmission in mobility-based IoT is one complex and challenging concern. The adoption of unified security architecture has been identified to prevent side-channel attacks in the IoT, which has been discussed extensively in developing security solutions. Despite blockchain’s apparent superiority in withstanding a wide range of security threats, a careful examination of the relevant literature reveals that some common pitfalls are associated with these methods. Therefore, the proposed scheme introduces a novel computational security framework wherein a branched and decentralized blockchain network is formulated to facilitate coverage from different variants of side-channel IoT attacks that are yet to be adequately reported. A unique blockchain-based authentication approach is designed to secure communication among mobile IoT devices using multiple stages of security implementation with Smart Agreement and physically unclonable functions. Analytical modeling with lightweight finite field encryption is used to create this framework in Python. The study’s benchmark results show that the proposed scheme offers 4% less processing time, 5% less computational overhead, 1% more throughput, 12% less latency, and 30% less energy consumption compared to existing blockchain methods.
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Chen, Xiangqian, Kia Makki, Kang Yen, and Niki Pissinou. "Attack Distribution Modeling and Its Applications in Sensor Network Security." EURASIP Journal on Wireless Communications and Networking 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/754252.
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Angelini, Marco, Graziano Blasilli, Tiziana Catarci, Simone Lenti, and Giuseppe Santucci. "Vulnus: Visual Vulnerability Analysis for Network Security." IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (January 2019): 183–92. http://dx.doi.org/10.1109/tvcg.2018.2865028.
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Khatri, Narayan, Rakesh Shrestha, and Seung Yeob Nam. "Security Issues with In-Vehicle Networks, and Enhanced Countermeasures Based on Blockchain." Electronics 10, no. 8 (April 8, 2021): 893. http://dx.doi.org/10.3390/electronics10080893.
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Modern vehicles are no longer simply mechanical devices. Connectivity between the vehicular network and the outside world has widened the security holes that hackers can use to exploit a vehicular network. Controller Area Network (CAN), FlexRay, and automotive Ethernet are popular protocols for in-vehicle networks (IVNs) and will stay in the industry for many more years. However, these protocols were not designed with security in mind. They have several vulnerabilities, such as lack of message authentication, lack of message encryption, and an ID-based arbitration mechanism for contention resolution. Adversaries can use these vulnerabilities to launch sophisticated attacks that may lead to loss of life and damage to property. Thus, the security of the vehicles should be handled carefully. In this paper, we investigate the security vulnerabilities with in-vehicle network protocols such as CAN, automotive Ethernet, and FlexRay. A comprehensive survey on security attacks launched against in-vehicle networks is presented along with countermeasures adopted by various researchers. Various algorithms have been proposed in the past for intrusion detection in IVNs. However, those approaches have several limitations that need special attention from the research community. Blockchain is a good approach to solving the existing security issues in IVNs, and we suggest a way to improve IVN security based on a hybrid blockchain.
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Al Ahmed, Mahmoud Tayseer, Fazirulhisyam Hashim, Shaiful Jahari Hashim, and Azizol Abdullah. "Authentication-Chains: Blockchain-Inspired Lightweight Authentication Protocol for IoT Networks." Electronics 12, no. 4 (February 8, 2023): 867. http://dx.doi.org/10.3390/electronics12040867.
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Internet of Things networks (IoT) are becoming very important in industrial, medical, and commercial applications. The security aspect of IoT networks is critical, especially the authentication of the devices in the network. The current security model in IoT networks uses centralized key exchange servers that present a security weak point. IoT networks need decentralized management for network security. Blockchain, with its decentralized model of authentication, can provide a solution for decentralized authentication in IoT networks. However, blockchain authentication models are known to be computationally demanding because they require complex mathematical calculations. In this paper, we present an Authentication-Chains protocol which is a lightweight decentralized protocol for IoT authentication based on blockchain distributed ledger. The proposed protocol arranges the nodes in clusters and creates an authentication blockchain for each cluster. These cluster chains are connected by another blockchain. A new consensus algorithm based on proof of identity authentication is adapted to the limited computational capabilities of IoT devices. The proposed protocol security performance is analyzed using cryptographic protocols verifier software and tested. Additionally, a test bed consisting of a Raspberry Pi network is presented to analyze the performance of the proposed protocol.
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Dasic, Dejan, Miljan Vucetic, Nemanja Ilic, Milos Stankovic, and Marko Beko. "Application of deep learning algorithms and architectures in the new generation of mobile networks." Serbian Journal of Electrical Engineering 18, no. 3 (2021): 397–426. http://dx.doi.org/10.2298/sjee2103397d.
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Operators of modern mobile networks are faced with significant challenges in providing the requested level of service to an ever increasing number of user entities. Advanced machine learning techniques based on deep architectures and appropriate learning methods are recognized as promising ways of tackling the said challenges in many aspects of mobile networks, such as mobile data and mobility analysis, network control, network security and signal processing. Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks. The paper continues with an overview of applications and services related to the new generation of mobile networks that employ deep learning methods. Finally, the paper presents practical use case of modulation classification as implementation of deep learning in an application essential for modern spectrum management. We complete this work by pinpointing future directions for research.
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Li, Wenchao, Ping Yi, Yue Wu, Li Pan, and Jianhua Li. "A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network." Journal of Electrical and Computer Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/240217.
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The Internet of Things has broad application in military field, commerce, environmental monitoring, and many other fields. However, the open nature of the information media and the poor deployment environment have brought great risks to the security of wireless sensor networks, seriously restricting the application of wireless sensor network. Internet of Things composed of wireless sensor network faces security threats mainly from Dos attack, replay attack, integrity attack, false routing information attack, and flooding attack. In this paper, we proposed a new intrusion detection system based onK-nearest neighbor (K-nearest neighbor, referred to as KNN below) classification algorithm in wireless sensor network. This system can separate abnormal nodes from normal nodes by observing their abnormal behaviors, and we analyse parameter selection and error rate of the intrusion detection system. The paper elaborates on the design and implementation of the detection system. This system has achieved efficient, rapid intrusion detection by improving the wireless ad hoc on-demand distance vector routing protocol (Ad hoc On-Demand Distance the Vector Routing, AODV). Finally, the test results show that: the system has high detection accuracy and speed, in accordance with the requirement of wireless sensor network intrusion detection.
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Zhu, Ying, Caixia Liu, Wei You, Yiming Zhang, and Weicheng Zhang. "A Business Process-Based Security Enhancement Scheme for the Network Function Service Access Procedure in the 5G Core Network." Electronics 12, no. 3 (January 23, 2023): 576. http://dx.doi.org/10.3390/electronics12030576.
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As the signaling processing center of 5G, the security and stability of the 5G Core Network (5GC) are of great importance for 5G. The current 5GC consists of multiple mutually independent Network Functions (NFs). However, the NF service access procedure does not match NF service requests and business processes. NFs can request authorized services for access at any time, which poses a security threat to NFs and user data. This paper proposes a security enhancement scheme for NF service access procedures based on the business process, which realizes the management of the NF business process. The NRF adds a token identifier field bound to the business process in the access token and establishes an access token repository to store the token identifier. NF Service Producer introduces an access token re-signature mechanism and a shared repository of responded access tokens. The security of the proposed scheme is verified by theoretical analysis and formal analysis, and the performance of the proposed scheme is evaluated in terms of response rate and resource consumption. The experimental results show that the proposed scheme can meet the security requirement with little efficiency degradation under the condition of increasing certain resource loss.
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D.N., Kolegov. "DP-MODEL APPLICATION FOR NETWORK SECURITY ANALYSIS." Prikladnaya diskretnaya matematika, no. 1 (June 1, 2008): 71–87. http://dx.doi.org/10.17223/20710410/1/12.
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Chen, Yue, Zi-Long Wu, and Ying-Ke Lei. "Individual Identification of Radar Emitters Based on a One-Dimensional LeNet Neural Network." Symmetry 13, no. 7 (July 7, 2021): 1215. http://dx.doi.org/10.3390/sym13071215.
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Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter through processing the received signals. By identifying the extracted fingerprint features, one can also identify the emitter to which the received signals belong. Due to differences in transmitter hardware, this fingerprint cannot be duplicated. Therefore, SEI plays an important role in the field of information security and can reduce the information leakages caused by key theft. This method can also be used in the military field to support communication countermeasures via emitter individual identification. In this paper, empirical mode decomposition is carried out for each radar pulse signal, and then the bispectral features are extracted. Dimensionality reduction is carried out according to the symmetry of the bispectral features. The features after dimensionality reduction are input into a one-dimensional LeNet neural network as the fingerprint features of the emitter, and the identification of 10 radar emitter sources is completed. Based on the verification of real signals, the SEI identification strategy in this paper achieved a recognition rate of 96.4% for 10 radar signals, 98.9% for 10 data emitter signals, and 88.93% for 5 communication radio signals.
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Lavrova, D. S., I. V. Alekseev, and A. A. Shtyrkina. "Security Analysis Based on Controlling Dependences of Network Traffic Parameters by Wavelet Transformation." Automatic Control and Computer Sciences 52, no. 8 (December 2018): 931–35. http://dx.doi.org/10.3103/s0146411618080187.
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Statkus, Arūnas, Šarūnas Paulikas, and Audrius Krukonis. "TCP Acknowledgment Optimization in Low Power and Embedded Devices." Electronics 10, no. 6 (March 10, 2021): 639. http://dx.doi.org/10.3390/electronics10060639.
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Paper investigates transport control protocol (TCP) acknowledgment (ACK) optimization in low power or embedded devices to improve their performance on high-speed links by limiting the ACK rate. Today the dominant protocol for interconnecting network devices is the TCP and it has a great influence on the entire network operation if the processing power of network devices is exhausted to the processing data from the TCP stack. Therefore, on high-speed not congested networks the bottleneck is no longer the network link but low-processing power network devices. A new ACK optimization algorithm has been developed and implemented in the Linux kernel. Proposed TCP stack modification minimizes the unneeded technical expenditure from TCP flow by reducing the number of ACKs. The results of performed experiments show that TCP ACK rate limiting leads to the noticeable decrease of CPU utilization on low power devices and an increase of TCP session throughput but does not impact other TCP QoS parameters, such as session stability, flow control, connection management, congestion control or compromises link security. Therefore, more resources of the low-power network devices could be allocated for high-speed data transfer.
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Zhu, Zhen, and Guofei Chai. "An Intrusion Intention Analysis Algorithm Based on Attack Graph." International Journal of Circuits, Systems and Signal Processing 15 (July 20, 2021): 643–50. http://dx.doi.org/10.46300/9106.2021.15.71.
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The discovery of intrusion intention is one of the challenging tasks faced by network security managers. To detect intrusion detections, this paper presents a domain-device attack graph, and collects and analyzes the underlying data of the network topology. On this basis, the attack graph Map was quantified by the Bayesian theory. The minimum weight spanning tree (Min-WFS) algorithm was adopted to automatically recognize the calculation cost of key devices in the network topology, providing an important basis for network maintenance. Experimental results show that the intrusion intentions can be effectively identified with the aid of the quantified domain-device attack graph Map, and this identification method is easy to implement.
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Huang, Tongyuan, Jia Xu, Shixin Tu, and Baoru Han. "Robust zero-watermarking scheme based on a depthwise overparameterized VGG network in healthcare information security." Biomedical Signal Processing and Control 81 (March 2023): 104478. http://dx.doi.org/10.1016/j.bspc.2022.104478.
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Hameed, Bilal Hashim, Anmar Yahya Taher, Raed Khalid Ibrahim, Adnan Hussein Ali, and Yasser Adnan Hussein. "Based on mesh sensor network: design and implementation of security monitoring system with Bluetooth technology." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1781. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1781-1790.
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One of the most <span>critical aspects to consider in wireless sensor networks is security, particularly in internet of things (IoT) implementations. Sensor network applications had risen in the past 5 years since these networks have been used in various parts of life (smart residential and commercial buildings, medical, and agriculture). In this study, we provide a novel network of sensors based on the Bluetooth network that may be used to protect commercial buildings. The Bluetooth type HC 06 was chosen since it has a low energy consumption and a communication range of 100 meters. Such security network includes motion sensors and control cameras that are controlled by an Arduino Nano microcontroller. The motion sensor's primary characteristics are solely applicable to humans, and the Arduino Nano is an open-source microcontroller. The key benefit of this research is that it demonstrates how to create a low-cost Bluetooth sensor security network with limited storage space for control movies.</span>
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Shi, Wei, Meichen Duan, Hui He, Liangliang Lin, Chen Yang, Chenhao Li, and Jizhong Zhao. "Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement." Applied Sciences 13, no. 3 (January 18, 2023): 1320. http://dx.doi.org/10.3390/app13031320.
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Action recognition is essential in security monitoring, home care, and behavior analysis. Traditional solutions usually leverage particular devices, such as smart watches, infrared/visible cameras, etc. These methods may narrow the application areas due to the risk of privacy leakage, high equipment cost, and over/under-exposure. Using wireless signals for motion recognition can effectively avoid the above problems. However, the motion recognition technology based on Wi-Fi signals currently has some defects, such as low resolution caused by narrow signal bandwidth, poor environmental adaptability caused by the multi-path effect, etc., which make it hard for commercial applications. To solve the above problems, we first propose and implement a position adaptive motion recognition method based on Wi-Fi feature enhancement, which is composed of an enhanced Wi-Fi features module and an enhanced convolution Transformer network. Meanwhile, we improve the generalization ability in the signal processing stage to avoid building an extremely complex model and reduce the demand for system hardware. To verify the generalization of the method, we implement real-world experiments using 9300 network cards and the PicoScenes software platform for data acquisition and processing. By contrast with the baseline method using original channel state information(CSI) data, the average accuracy of our algorithm is improved by 14% in different positions and over 16% in different orientations. Meanwhile, our method has best performance with an accuracy of 90.33% compared with the existing models on public datasets WiAR and WiDAR.
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Memon, Mudasar, Navrati Saxena, Abhishek Roy, and Dong Shin. "Backscatter Communications: Inception of the Battery-Free Era—A Comprehensive Survey." Electronics 8, no. 2 (January 26, 2019): 129. http://dx.doi.org/10.3390/electronics8020129.
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The ever increasing proliferation of wireless objects and consistent connectivity demands are creating significant challenges for battery-constrained wireless devices. The vision of massive IoT, involving billions of smart objects to be connected to the cellular network, needs to address the problem of uninterrupted power consumption while taking advantage of emerging high-frequency 5G communications. The problem of limited battery power motivates us to utilize radio frequency (RF) signals as the energy source for battery-free communications in next-generation wireless networks. Backscatter communication (BackCom) makes it possible to harvest energy from incident RF signals and reflect back parts of the same signals while modulating the data. Ambient BackCom (Amb-BackCom) is a type of BackCom that can harvest energy from nearby WiFi, TV, and cellular RF signals to modulate information. The objective of this article is to review BackCom as a solution to the limited battery life problem and enable future battery-free communications for combating the energy issues for devices in emerging wireless networks. We first highlight the energy constraint in existing wireless communications. We then investigate BackCom as a practical solution to the limited battery life problem. Subsequently, in order to take the advantages of omnipresent radio waves, we elaborate BackCom tag architecture and various types of BackCom. To understand encoding and data extraction, we demonstrate signal processing aspects that cover channel coding, interference, decoding, and signal detection schemes. Moreover, we also describe BackCom communication modes, modulation schemes, and multiple access techniques to accommodate maximum users with high throughput. Similarly, to mitigate the increased network energy, adequate data and power transfer schemes for BackCom are elaborated, in addition to reliability, security, and range extension. Finally, we highlight BackCom applications with existing research challenges and future directions for next-generation 5G wireless networks.
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Chugh, Neeraj, Geetam Singh Tomar, Robin Singh Bhadoria, and Neetesh Saxena. "A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks." Electronics 10, no. 14 (July 9, 2021): 1635. http://dx.doi.org/10.3390/electronics10141635.
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To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources.
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Rehman, Attique Ur, Muhammad Sajid Mahmood, Shoaib Zafar, Muhammad Ahsan Raza, Fahad Qaswar, Sumayh S. Aljameel, Irfan Ullah Khan, and Nida Aslam. "A Survey on MAC-Based Physical Layer Security over Wireless Sensor Network." Electronics 11, no. 16 (August 12, 2022): 2529. http://dx.doi.org/10.3390/electronics11162529.
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Physical layer security for wireless sensor networks (WSNs) is a laborious and highly critical issue in the world. Wireless sensor networks have great importance in civil and military fields or applications. Security of data/information through wireless medium remains a challenge. The data that we transmit wirelessly has increased the speed of transmission rate. In physical layer security, the data transfer between source and destination is not confidential, and thus the user has privacy issues, which is why improving the security of wireless sensor networks is a prime concern. The loss of physical security causes a great threat to a network. We have various techniques to resolve these issues, such as interference, noise, fading in the communications, etc. In this paper we have surveyed the different parameters of a security design model to highlight the vulnerabilities. Further we have discussed the various attacks on different layers of the TCP/IP model along with their mitigation techniques. We also elaborated on the applications of WSNs in healthcare, military information integration, oil and gas. Finally, we have proposed a solution to enhance the security of WSNs by adopting the alpha method and handshake mechanism with encryption and decryption.
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Wang, Weizheng, Zhuo Deng, and Jin Wang. "Enhancing Sensor Network Security with Improved Internal Hardware Design." Sensors 19, no. 8 (April 12, 2019): 1752. http://dx.doi.org/10.3390/s19081752.
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With the rapid development of the Internet-of-Things (IoT), sensors are being widely applied in industry and human life. Sensor networks based on IoT have strong Information transmission and processing capabilities. The security of sensor networks is progressively crucial. Cryptographic algorithms are widely used in sensor networks to guarantee security. Hardware implementations are preferred, since software implementations offer lower throughout and require more computational resources. Cryptographic chips should be tested in a manufacturing process and in the field to ensure their quality. As a widely used design-for-testability (DFT) technique, scan design can enhance the testability of the chips by improving the controllability and observability of the internal flip-flops. However, it may become a backdoor to leaking sensitive information related to the cipher key, and thus, threaten the security of a cryptographic chip. In this paper, a secure scan test architecture was proposed to resist scan-based noninvasive attacks on cryptographic chips with boundary scan design. Firstly, the proposed DFT architecture provides the scan chain reset mechanism by gating a mode-switching detection signal into reset input of scan cells. The contents of scan chains will be erased when the working mode is switched between test mode and functional mode, and thus, it can deter mode-switching based noninvasive attacks. Secondly, loading the secret key into scan chains of cryptographic chips is prohibited in the test mode. As a result, the test-mode-only scan attack can also be thwarted. On the other hand, shift operation under functional mode is disabled to overcome scan attack in the functional mode. The proposed secure scheme ensures the security of cryptographic chips for sensor networks with extremely low area penalty.
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Algarni, Sultan, Fathy Eassa, Khalid Almarhabi, Abdullah Algarni, and Aiiad Albeshri. "BCNBI: A Blockchain-Based Security Framework for Northbound Interface in Software-Defined Networking." Electronics 11, no. 7 (March 23, 2022): 996. http://dx.doi.org/10.3390/electronics11070996.
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Software-defined networking (SDN) has emerged as a flexible and programmable network architecture that takes advantage of the benefits of global visibility and centralized control over a network. One of the main properties of the SDN architecture is the ability to offer a northbound interface (NBI), which enables network applications to access the SDN controller resources. However, the NBI can be compromised by a malicious application due to the lack of standardization and security aspects in the most current NBI designs. Therefore, in this paper, we propose a novel comprehensive security solution for securing the application–controller interface, named BCNBI. We propose a controller-independent lightweight blockchain architecture and exploit the security features of blockchain while limiting the blockchain’s computational overhead. BCNBI automatically verifies application and SDN controller credentials through token-based authentication. The proposed solution enforces fine-grained access control for each application’s API request and classifies the permission set into strict and normal policies, in order to add an extra level of security. In addition, the trustworthiness of applications is evaluated in order to prevent malicious activities. We implemented our blockchain-based solution to analyze its security, based on the confidentiality–integrity–availability model criteria, and evaluated the introduced overhead in terms of processing time and packet overhead. The experimental results demonstrate that the BCNBI can effectively secure the NBI, based on the fundamental security goals, while introducing insignificant overhead.
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Ruchkin, V. N., B. V. Kostrov, and V. A. Fulin. "Intelligent Security Strategy Based on the Selection of the Computer and Neural Network Architecture." Automatic Control and Computer Sciences 56, no. 8 (December 2022): 970–80. http://dx.doi.org/10.3103/s014641162208020x.
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Zhu, Mengyun, Ximin Fan, Weijing Liu, Jianying Shen, Wei Chen, Yawei Xu, and Xuejing Yu. "Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison." Journal of Healthcare Engineering 2021 (December 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/1336762.
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This paper combines echocardiographic signal processing and artificial intelligence technology to propose a deep neural network model adapted to echocardiographic signals to achieve left atrial volume measurement and automatic assessment of pulmonary veins efficiently and quickly. Based on the echocardiographic signal generation mechanism and detection method, an experimental scheme for the echocardiographic signal acquisition was designed. The echocardiographic signal data of healthy subjects were measured in four different experimental states, and a database of left atrial volume measurements and pulmonary veins was constructed. Combining the correspondence between ECG signals and echocardiographic signals in the time domain, a series of preprocessing such as denoising, feature point localization, and segmentation of the cardiac cycle was realized by wavelet transform and threshold method to complete the data collection. This paper proposes a comparative model based on artificial intelligence, adapts to the characteristics of one-dimensional time-series echocardiographic signals, automatically extracts the deep features of echocardiographic signals, effectively reduces the subjective influence of manual feature selection, and realizes the automatic classification and evaluation of human left atrial volume measurement and pulmonary veins under different states. The experimental results show that the proposed BP neural network model has good adaptability and classification performance in the tasks of LV volume measurement and pulmonary vein automatic classification evaluation and achieves an average test accuracy of over 96.58%. The average root-mean-square error percentage of signal compression is only 0.65% by extracting the coding features of the original echocardiographic signal through the convolutional autoencoder, which completes the signal compression with low loss. Comparing the training time and classification accuracy of the LSTM network with the original signal and encoded features, the experimental results show that the AI model can greatly reduce the model training time cost and achieve an average accuracy of 97.97% in the test set and increase the real-time performance of the left atrial volume measurement and pulmonary vein evaluation as well as the security of the data transmission process, which is very important for the comparison of left atrial volume measurement and pulmonary vein. It is of great practical importance to compare left atrial volume measurements with pulmonary veins.
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Mohtadzar, Nur Alia Athirah, and Shigeru Takayama. "Revelation of Body Behavior Based on Arm Motion Measurement in Wireless Body Area Network System." Applied Mechanics and Materials 833 (April 2016): 179–84. http://dx.doi.org/10.4028/www.scientific.net/amm.833.179.
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Wireless Body Area Network or known as BAN, is a system consists of various kinds of wearable sensors to measure condition of human body. Wrist, waist and shoulder modules from BAN system can help to monitor, analyze and provide advice to the user in order to perform a moderate exercise. The availability of small, low-cost networked sensors combined with advanced signal processing and information extraction is driving a revolution in physiological monitoring and intervention. BAN system is enabling technologies for accurate measurements in healthcare systems, enhance sports and fitness training, life-style monitoring and individualized security.
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Sun, Hongzhe, Jian Wang, Chen Chen, Zhi Li, and Jinjin Li. "ISSA-ELM: A Network Security Situation Prediction Model." Electronics 12, no. 1 (December 21, 2022): 25. http://dx.doi.org/10.3390/electronics12010025.
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To resolve the problems of low prediction accuracy and slow convergence speed of traditional extreme learning machines in network security situation prediction methods, we combine a meta-heuristic search algorithm with neural networks and propose a prediction method based on the improved sparrow search algorithm optimization of an extreme learning machine. Firstly, the initial population is initialized by cat-mapping chaotic sequences to enhance the randomness and ergodicity of the initial population and improve the global search ability of the algorithm. Secondly, the Cauchy mutation and tent chaos disturbance are introduced to expand the local search ability, so that the individuals caught in the local extremum can jump out of the limit and continue the search. Finally, the explorer-follower number adaptive adjustment strategy is proposed to enhance the global search ability in the early stage and the local depth mining ability in the later stage of the algorithm by using the change of the explorer and follower numbers in each stage to improve the optimization-seeking accuracy of the algorithm. The improvement not only guarantees the diversity of the population, but also makes up for the defect that the sparrow search algorithm is easily trapped in the local optima in later iterations, and greatly improves the accuracy of the network security situation prediction.
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Hussain, Muhammad Zunnurain, and Zurina Mohd Hanapi. "Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review." Electronics 12, no. 3 (January 17, 2023): 482. http://dx.doi.org/10.3390/electronics12030482.
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The Wireless Sensor Network in the Internet of Things (WSN-IoT) has been flourishing as another global breakthrough over the past few years. The WSN-IoT is reforming the way we live today by spreading through all areas of life, including the dangerous demographic aging crisis and the subsequent decline of jobs. For a company to increase revenues and cost-effectiveness growth should be customer-centered and agile within an organization. WSN-IoT networks have simultaneously faced threats, such as sniffing, spoofing, and intruders. However, WSN-IoT networks are often made up of multiple embedded devices (sensors and actuators) with limited resources that are joined via various connections in a low-power and lossy manner. However, to our knowledge, no research has yet been conducted into the security methods. Recently, a Contiki operating system’s partial implementation of Routing Protocol for Low Power & Lossy Network RPL’s security mechanisms was published, allowing us to evaluate RPL’s security methods. This paper presents a critical analysis of security issues in the WSN-IoT and applications of WSN-IoT, along with network management details using machine learning. The paper gives insights into the Internet of Things in Low Power Networks (IoT-LPN) architecture, research challenges of the Internet of Things in Low Power Networks, network attacks in WSN-IoT infrastructures, and the significant WSN-IoT objectives that need to be accompanied by current WSN-IoT frameworks. Several applied WSN-IoT security mechanisms and recent contributions have been considered, and their boundaries have been stated to be a significant research area in the future. Moreover, various low-powered IoT protocols have been further discussed and evaluated, along with their limitations. Finally, a comparative analysis is performed to assess the proposed work’s performance. The study shows that the proposed work covers a wide range of factors, whereas the rest of the research in the literature is limited.