Relevant bibliographies by topics / Classification of network devices

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Classification of network devices'

Author: Grafiati
Published: 28 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Classification of network devices"

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Mhetre, Nalini A., Arvind V. Deshpande, and Parikshit Narendra Mahalle. "Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 135–42. http://dx.doi.org/10.35940/ijeat.e2688.0610521.

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Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is proactive device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources.
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Ganesan, Elaiyasuriyan, I.-Shyan Hwang, Andrew Tanny Liem, and Mohammad Syuhaimi Ab-Rahman. "SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models." Photonics 8, no. 6 (June 4, 2021): 201. http://dx.doi.org/10.3390/photonics8060201.

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Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.
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Bezerra, Vitor Hugo, Victor Guilherme Turrisi da Costa, Sylvio Barbon Junior, Rodrigo Sanches Miani, and Bruno Bogaz Zarpelão. "IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices." Sensors 19, no. 14 (July 19, 2019): 3188. http://dx.doi.org/10.3390/s19143188.

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Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architecture based on HTTPS, which prevents the IoT device from being overloaded by the training activities. To analyse the device’s behaviour, the approach extracts features from the device’s CPU utilisation and temperature, memory consumption, and number of running tasks, meaning that it does not make use of network traffic data. To test our approach, we used an experimental IoT setup containing a device compromised by bot malware. Multiple scenarios were made, including three different IoT device profiles and seven botnets. Four one-class algorithms (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine) were evaluated. The results show the proposed system has a good predictive performance for different botnets, achieving a mean F1-score of 94% for the best performing algorithm, the Local Outlier Factor. The system also presented a low impact on the device’s energy consumption, and CPU and memory utilisation.
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Li, Xiu, Rujiao Long, Jiangpeng Yan, Kun Jin, and Jihae Lee. "TANet: A Tiny Plankton Classification Network for Mobile Devices." Mobile Information Systems 2019 (April 3, 2019): 1–8. http://dx.doi.org/10.1155/2019/6536925.

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This paper is devoted to a lightweight convolutional neural network based on the attention mechanism called the tiny attention network (TANet). The TANet consists of three main parts termed as a reduction module, self-attention operation, and group convolution. The reduction module alleviates information loss caused by the pooling operation. The new parameter-free self-attention operation makes the model to focus on learning important parts of images. The group convolution achieves model compression and multibranch fusion. Using the main parts, the proposed network enables efficient plankton classification on mobile devices. The performance of the proposed network is evaluated on the Plankton dataset collected by Oregon State University’s Hatfield Marine Science Center. The results show that TANet outperforms other deep models in speed (31.8 ms per image), size (648 kB, the size of the hard disk space occupied by the model), and accuracy (Top-1 76.5%, Top-5 96.3%).
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Niewiadomska-Szynkiewicz, Ewa. "Localization in wireless sensor networks: Classification and evaluation of techniques." International Journal of Applied Mathematics and Computer Science 22, no. 2 (June 1, 2012): 281–97. http://dx.doi.org/10.2478/v10006-012-0021-x.

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Localization in wireless sensor networks: Classification and evaluation of techniques Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices. These devices are networked through setting up a Wireless Sensor Network (WSN). Sensors that form a WSN are expected to be remotely deployed in large numbers and to self-organize to perform distributed sensing and acting tasks. WSNs are growing rapidly in both size and complexity, and it is becoming increasingly difficult to develop and investigate such large and complex systems. In this paper we provide a brief introduction to WSN applications, i.e., properties, limitations and basic issues related to WSN design and development. We focus on an important aspect of the design: accurate localization of devices that form the network. The paper presents an overview of localization strategies and attempts to classify different techniques. A set of properties by which localization systems are evaluated are examined. We then describe a number of existing localization systems, and discuss the results of performance evaluation of some of them through simulation and experiments using a testbed implementation.
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Kim, Jiyeon, Minsun Shim, Seungah Hong, Yulim Shin, and Eunjung Choi. "Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning." Applied Sciences 10, no. 19 (October 8, 2020): 7009. http://dx.doi.org/10.3390/app10197009.

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As the number of Internet of Things (IoT) devices connected to the network rapidly increases, network attacks such as flooding and Denial of Service (DoS) are also increasing. These attacks cause network disruption and denial of service to IoT devices. However, a large number of heterogenous devices deployed in the IoT environment make it difficult to detect IoT attacks using traditional rule-based security solutions. It is challenging to develop optimal security models for each type of the device. Machine learning (ML) is an alternative technique that allows one to develop optimal security models based on empirical data from each device. We employ the ML technique for IoT attack detection. We focus on botnet attacks targeting various IoT devices and develop ML-based models for each type of device. We use the N-BaIoT dataset generated by injecting botnet attacks (Bashlite and Mirai) into various types of IoT devices, including a Doorbell, Baby Monitor, Security Camera, and Webcam. We develop a botnet detection model for each device using numerous ML models, including deep learning (DL) models. We then analyze the effective models with a high detection F1-score by carrying out multiclass classification, as well as binary classification, for each model.
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Lu, Peng, Yang Gao, Hao Xi, Yabin Zhang, Chao Gao, Bing Zhou, Hongpo Zhang, Liwei Chen, and Xiaobo Mao. "KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement." Journal of Healthcare Engineering 2021 (April 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6684954.

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Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep neural networks have become a popular method to efficiently and accurately simulate nonlinear patterns of ECG data in a data-driven manner but require more resources. Therefore, it is crucial to design deep learning (DL) algorithms that are more suitable for resource-constrained mobile devices. In this paper, KecNet, a lightweight neural network construction scheme based on domain knowledge, is proposed to model ECG data by effectively leveraging signal analysis and medical knowledge. To evaluate the performance of KecNet, we use the Association for the Advancement of Medical Instrumentation (AAMI) protocol and the MIT-BIH arrhythmia database to classify five arrhythmia categories. The result shows that the ACC, SEN, and PRE achieve 99.31%, 99.45%, and 98.78%, respectively. In addition, it also possesses high robustness to noisy environments, low memory usage, and physical interpretability advantages. Benefiting from these advantages, KecNet can be applied in practice, especially wearable and lightweight mobile devices for arrhythmia classification.
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Feng, Kai, Xitian Pi, Hongying Liu, and Kai Sun. "Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network." Applied Sciences 9, no. 9 (May 7, 2019): 1879. http://dx.doi.org/10.3390/app9091879.

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Myocardial infarction is one of the most threatening cardiovascular diseases for human beings. With the rapid development of wearable devices and portable electrocardiogram (ECG) medical devices, it is possible and conceivable to detect and monitor myocardial infarction ECG signals in time. This paper proposed a multi-channel automatic classification algorithm combining a 16-layer convolutional neural network (CNN) and long-short term memory network (LSTM) for I-lead myocardial infarction ECG. The algorithm preprocessed the raw data to first extract the heartbeat segments; then it was trained in the multi-channel CNN and LSTM to automatically learn the acquired features and complete the myocardial infarction ECG classification. We utilized the Physikalisch-Technische Bundesanstalt (PTB) database for algorithm verification, and obtained an accuracy rate of 95.4%, a sensitivity of 98.2%, a specificity of 86.5%, and an F1 score of 96.8%, indicating that the model can achieve good classification performance without complex handcrafted features.
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Et. al., Gopal Patil,. "REVIEW THE DEEP LEARNING TECHNIQUE FOR MISSING DATA CLASSIFICATION IN IOT APPLICATIONS FOR NETWORK PERFORMANCE IMPROVEMENT." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 25, 2021): 365–69. http://dx.doi.org/10.17762/itii.v9i2.356.

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In order to ensure product safety and increase production quality, The construction of mine Internet of Things networks continues to accelerate mining enterprises. Given the large increase in the number of networked devices connectivity capability in the mine, there is considerable strain on the mine network communication facilities. We suggest an Innovative Solution Using Deep Learning for Missing Data Classification in IoT Network Performance Enhancement System Market Classifier based on neural networks to improve the quality of service in the connectivity infrastructure of mine networks. The classifier uses a transformation wavelet to delete the data flow and to build compliance characteristics to identify the market categories of the system.Owing to the findings of the classification, the system changes the specifications of the network services given to the terminal equipment in a versatile manner. In this way, the system's network capacity can be fairly distributed. We assess the output of the classifier model using the test data collection. We review the deep learning technique in IoT applications for Network Improvement for missing data classification
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Cotrim, Jeferson Rodrigues, and João Henrique Kleinschmidt. "LoRaWAN Mesh Networks: A Review and Classification of Multihop Communication." Sensors 20, no. 15 (July 31, 2020): 4273. http://dx.doi.org/10.3390/s20154273.

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The growth of the Internet of Things (IoT) led to the deployment of many applications that use wireless networks, like smart cities and smart agriculture. Low Power Wide Area Networks (LPWANs) meet many requirements of IoT, such as energy efficiency, low cost, large coverage area, and large-scale deployment. Long Range Wide Area Network (LoRaWAN) networks are one of the most studied and implemented LPWAN technologies, due to the facility to build private networks with an open standard. Typical LoRaWAN networks are single-hop in a star topology, composed of end-devices that transmit data directly to gateways. Recently, several studies proposed multihop LoRaWAN networks, thus forming wireless mesh networks. This article provides a review of the state-of-the-art multihop proposals for LoRaWAN. In addition, we carried out a comparative analysis and classification, considering technical characteristics, intermediate devices function, and network topologies. This paper also discusses open issues and future directions to realize the full potential of multihop networking. We hope to encourage other researchers to work on improving the performance of LoRaWAN mesh networks, with more theoretical and simulation analysis, as well as practical deployments.
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Dissertations / Theses on the topic "Classification of network devices"

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Eis, Pavel. "Datová sada pro klasifikaci síťových zařízení pomocí strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445543.

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Automatic classification of devices in computer network can be used for detection of anomalies in a network and also it enables application of security policies per device type. The key to creating a device classifier is a quality data set, the public availability of which is low and the creation of a new data set is difficult. The aim of this work is to create a tool, that will enable automated annotation of the data set of network devices and to create a classifier of network devices that uses only basic data from network flows. The result of this work is a modular tool providing automated annotation of network devices using system ADiCT of Cesnet's association, search engines Shodan and Censys, information from PassiveDNS, TOR, WhoIs, geolocation database and information from blacklists. Based on the annotated data set are created several classifiers that classify network devices according to the services they use. The results of the work not only significantly simplify the process of creating new data sets of network devices, but also show a non-invasive approach to the classification of network devices.
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Sleem, Lama. "Design and implementation of lightweight and secure cryptographic algorithms for embedded devices." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD018.

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Nous vivons actuellement dans une ère avec sans cesse de nouveaux appareils technologiques (smartphone, réseaux de capteurs sans fil, aux caméras haute résolution, etc). En partant des médias sociaux, en passant par des caméras de surveillance très puissantes, et sans oublier la surveillance de la santé en temps réel, on constate qu'une grande quantité de données est stockée dans le cloud et les serveurs. Cela représente un grand défi de stockage et de transmission, en particulier dans les plates-formes aux ressources limitées qui sont caractérisées par : (a) des capacités de calcul limitées, (b) une source d'énergie limitées et (c) des infrastructures ouvertes qui transmettent des données sur des réseaux sans fil peu fiables. Dans cette thèse, nous nous concentrons sur l'amélioration de la sécurité des contenus multimédia transmis sur des plates-formes à capacité de calcul limitée, tout en préservant un niveau de sécurité élevé. Dans la première partie, nous avons étudié les réseaux ad hoc véhiculaire. Nous avons proposé un état de l'art qui permet de résumer la plupart des travaux récents et d'explorer presque tous les aspects de ce domaine en illustrant les différents aspects que possède cette plateforme. Ensuite, afin de proposer une nouvelle solution de sécurité et de valider sa robustesse et le niveau de caractère aléatoire d'une image chiffrée, nous avons proposé un test simple et efficace. Celui-ci est basé sur des outils pour tester statistiquement le caractère aléatoire de nombres pseudo aléatoires, TestU01 et Practrand. Après avoir effectué ces tests sur des algorithmes de chiffrement bien connus, certaines failles ont été exposées et une nouvelle proposition visant à améliorer le système de chiffrement ultra-léger Speck est proposée. La principale contribution de ce travail est d'obtenir une meilleure version par rapport à Speck. Dans cette nouvelle proposition, appelée Speck-R, nous utilisons seulement 7 itérations contrairement à Speck qui en utilise 26 et nous réduisons le temps d'exécution d'au moins 50%. Tout d'abord, nous validons que Speck-R répond aux tests de statistiques pour mesurer l'aléatoire, proposés précédemment. De plus, nous avons rajouté un système de clé dynamique qui procure plus de sécurité contre les attaques liées à la clé. Speck-R a été implémenté sur différentes cartes de type arduino et dans tous les cas, Speck-R était plus rapide que Speck. Ensuite, afin de prouver que ce chiffrement peut être utilisé pour sécuriser les images, en particulier dans les réseaux VANETS/IoV, plusieurs tests ont été effectués et les résultats montrent que Speck-R possède effectivement le haut niveau de sécurité souhaité. Des expérimentations valident notre proposition du point de vue de la sécurité et de la performance et démontrent la robustesse du système proposé face aux types d'attaques les plus connus
Living in an era where new devices are astonishing considering their high capabilities, new visions and terms have emerged. Moving to smart phones, Wireless Sensor Networks, high-resolution cameras, pads and much more, has mandated the need to rethink the technological strategy that is used today. Starting from social media, where apparently everything is being exposed, moving to highly powerful surveillance cameras, in addition to real time health monitoring, it can be seen that a high amount of data is being stored in the Cloud and servers. This introduced a great challenge for their storage and transmission especially in the limited resourced platforms that are characterized by: (a) limited computing capabilities, (b) limited energy and source of power and (c) open infrastructures that transmit data over wireless unreliable networks. One of the extensively studied platforms is the Vehicular Ad-hoc Networks which tends to have many limitations concerning the security field. In this dissertation, we focus on improving the security of transmitted multimedia contents in different limited platforms, while preserving a high security level. Limitations of these platforms are taken into consideration while enhancing the execution time of the secure cipher. Additionally, if the proposed cipher is to be used for images, the intrinsic voluminous and complex nature of the managed images is also taken into account. In the first part, we surveyed one of the limited platforms that is interesting for many researchers, which is the Vehicular Ad-hoc Networks. In order to pave the way for researchers to find new efficient security solutions, it is important to have one reference that can sum most of the recent works. It almost investigates every aspect in this field shedding the light over different aspects this platform possesses. Then, in order to propose any new security solution and validate its robustness and the level of randomness of the ciphered image, a simple and efficient test is proposed. This test proposes using the randomness tools, TestU01 and Practrand, in order to assure a high level of randomness. After running these tests on well known ciphers, some flaws were exposed. Proceeding to the next part, a novel proposal for enhancing the well-known ultra lightweight cipher scheme, Speck, is proposed. The main contribution of this work is to obtain a better version compared to Speck. In this proposal, 26 rounds in Speck were reduced to 7 rounds in Speck-R while enhancing the execution time by at least 50%. First, we validate that Speck-R meets the randomness tests that are previously proposed. Additionally, a dynamic substitution layer adds more security against key related attacks and highly fortifies the cipher. Speck-R was implemented on different limited arduino chips and in all cases, Speck-R was ahead of Speck. Then, in order to prove that this cipher can be used for securing images, especially in VANETS/IoV, where images can be extensively re/transmitted, several tests were exerted and results showed that Speck-R indeed possesses the high level of security desired in any trusted cipher. Extensive experiments validate our proposal from both security and performance point of views and demonstrate the robustness of the proposed scheme against the most-known types of attacks
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Erlandsson, Niklas. "Utilizing machine learning in wildlife camera traps for automatic classification of animal species : An application of machine learning on edge devices." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104952.

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A rapid global decline in biodiversity has been observed in the past few decades, especially in large vertebrates and the habitats supporting these animal populations. This widely accepted fact has made it very important to understand how animals respond to modern ecological threats and to understand the ecosystems functions. The motion activated camera (also known as a camera trap) is a common tool for research in this field, being well-suited for non-invasive observation of wildlife. The images captured by camera traps in biological studies need to be classified to extract information, a traditionally manual process that is time intensive. Recent studies have shown that the use of machine learning (ML) can automate this process while maintaining high accuracy. Until recently the use of machine learning has required significant computing power, relying on data being processed after collection or transmitted to the cloud. This need for connectivity introduces potentially unsustainable overheads that can be addressed by placing computational resources on the camera trap and processing data locally, known as edge computing. Including more computational power in edge and IoT devices makes it possible to keep the computation and data storage on the edge, commonly referred to as edge computing. Applying edge computing to the camera traps enables the use of ML in environments with slow or non-existent network accesss since their functionality does not rely on the need for connectivity. This project shows the feasibility of running machine learning algorithms for the purpose of species identification on low-cost hardware with similar power to what is commonly found in edge and IoT devices, achieving real-time performance and maintaining high energy efficiency sufficient for more than 12 hours of runtime on battery power. Accuracy results were mixed, indicating the need for more tailor-made network models for performing this task and the importance of high quality images for classification.
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Luckhardt, Christoph [Verfasser]. "Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks / Christoph Luckhardt." Kassel : Universitätsbibliothek Kassel, 2013. http://d-nb.info/1043361863/34.

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Akarapu, Deepika. "Object Identification Using Mobile Device for Visually Impaired Person." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628092619349812.

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Wolf, Robert, Niko Joram, Stefan Schumann, and Frank Ellinger. "Dual-band impedance transformation networks for integrated power amplifiers." Cambridge University Press, 2016. https://tud.qucosa.de/id/qucosa%3A70680.

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This paper shows that the two most common impedance transformation networks for power amplifiers (PAs) can be designed to achieve optimum transformation at two frequencies. Hence, a larger bandwidth for the required impedance transformation ratio is achieved. A design procedure is proposed, which takes imperfections like losses into account. Furthermore, an analysis method is presented to estimate the maximum uncompressed output power of a PA with respect to frequency. Based on these results, a fully integrated PA with a dual-band impedance transformation network is designed and its functionality is proven by large signal measurement results. The amplifier covers the frequency band from 450 MHz to 1.2 GHz (3 dB bandwidth of the output power and efficiency), corresponding to a relative bandwidth of more than 100%. It delivers 23.7 dBm output power in the 1 dB compression point, having a power-added efficiency of 33%.
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Kühnert, Wolfram. "Dynamic Devices Network Architecture." [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10952962.

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Alexander, David. "A Network Metadata Infrastructure for Locating Network Devices." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1088176648.

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Zenteno, Efrain. "Vector Measurements for Wireless Network Devices." Licentiate thesis, KTH, Signalbehandling, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-111863.

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Wireless networks are an iconic technology of today’s modern era, theyare present in our daily activities as can be exemplified by cellular communications,wi-fi, bluetooth, and others. Vector measurements play an importantrole in the design, simulation, and testing of wireless networks and are usedto characterize key devices operating in the radio interface, such as amplifiers,filters, and mixers.Accurate characterization is the key for improving the capacity and efficiencyof wireless networks. As the demand for network capacity continuouslyincreases, the accuracy of vector measurements must also improve. Further,it is anticipated that such trends will continue in the years to come. Consequently,the wireless industry needs to include nonlinear behavior in theircharacterization and analysis, to assess and guaranty the operation of the devices,and to comply to the specifications from governmental regulations. Incontrast to linear behavior, nonlinear behavior presents an additional bandwidthrequirement because the signal bandwidth grows when it passes throughnonlinear devices. In this thesis, vector measurements for devices operatingin wireless networks are studied, emphasizing a synthetic approach for theinstrumentation. This approach enables the use of digital post-processing algorithms,which enhances the measurement accuracy and/or speed and canovercome hardware impairments. This thesis presents the design of a vectorialmeasurement system for wireless devices considering the aforementionedtrends and requirements. It also explores the advantages of the proposedapproach, describes its limitations, and discusses the digital signal processingalgorithms used to reach its final functionality. Finally, measurement resultsof the proposed setup are presented, analyzed and compared to those of modernindustrial instruments.

QC 20130204

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Omer, Mahgoub Saied Khalid. "Network Latency Estimation Leveraging Network Path Classification." Thesis, KTH, Network Systems Laboratory (NS Lab), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229955.

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With the development of the Internet, new network services with strict network latency requirements have been made possible. These services are implemented as distributed systems deployed across multiple geographical locations. To provide low response time, these services require knowledge about the current network latency. Unfortunately, network latency among geo-distributed sites often change, thus distributed services rely on continuous network latency measurements. One goal of such measurements is to differentiate between momentary latency spikes from relatively long-term latency changes. The differentiation is achieved through statistical processing of the collected samples. This approach of high-frequency network latency measurements has high overhead, slow to identify network latency changes and lacks accuracy. We propose a novel approach for network latency estimation by correlating network paths to network latency. We demonstrate that network latency can be accurately estimated by first measuring and identifying the network path used and then fetching the expected latency for that network path based on previous set of measurements. Based on these principles, we introduce Sudan traceroute, a network latency estimation tool. Sudan traceroute can be used to both reduce the latency estimation time as well as to reduce the overhead of network path measurements. Sudan traceroute uses an improved path detection mechanism that sends only a few carefully selected probes in order to identify the current network path. We have developed and evaluated Sudan traceroute in a test environment and evaluated the feasibility of Sudan traceroute on real-world networks using Amazon EC2. Using Sudan traceroute we have shortened the time it takes for hosts to identify network latency level changes compared to existing approaches.
Med utvecklingen av Internet har nya nätverkstjänster med strikta fördröjningskrav möjliggjorts. Dessa tjänster är implementerade som distribuerade system spridda över flera geografiska platser. För att tillgodose låg svarstid kräver dessa tjänster kunskap om svarstiden i det nuvarande nätverket. Tyvärr ändras ofta nätverksfördröjningen bland geodistribuerade webbplatser, således är distribuerade tjänster beroende av kontinuerliga mätvärden för nätverksfördröjning. Ett mål med sådana mätningar är att skilja mellan momenta ökade svarstider från relativt långsiktiga förändringar av svarstiden. Differentieringen uppnås genom statistisk bearbetning av de samlade mätningarna. Denna högfrekventa insamling av mätningar av nätverksfördröjningen har höga overheadkostnader, identifierar ändringar långsamt och saknar noggrannhet. Vi föreslår ett nytt tillvägagångssätt för beräkningen av nätverksfördröjning genom att korrelera nätverksvägar till nätverksfördröjning. Vi visar att nätverksfördröjningen kan vara exakt uppskattad genom att man först mäter och identifierar den nätverksväg som används och sedan hämtar den förväntade fördröjningen för den nätverksvägen baserad på en tidigare uppsättning av mätningar. Baserat på dessa principer introducerar vi Sudan traceroute, ett Verktyg för att uppskatta nätverksfördröjning. Sudan traceroute kan användas för att både minska tiden att uppskatta fördröjningen samt att minska overhead för mätningarna i nätverket. Sudan traceroute använder en förbättrad vägdetekteringsmekanism som bara skickar några försiktigt valda prober för att identifiera den aktuella vägen i nätverket. Vi har utvecklat och utvärderat Sudan traceroute i en testmiljö och utvärderade genomförbarheten av Sudan traceroute i verkliga nätverk med hjälp av Amazon EC2. Med hjälp av Sudan traceroute har vi förkortat den tid det tar för värdar att identifiera nätverksfördröjnings förändringar jämfört med befintliga tillvägagångssätt.
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Books on the topic "Classification of network devices"

1

Interconnecting Cisco network devices. Indianapolis, IN: Cisco Press, 2008.

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Rusen, Ciprian Adrian. Network your computers & devices step by step. Sebastopol, CA: O'Reilly Media, 2010.

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McQuerry, Steve. Authorized self-study guide: Interconnecting Cisco network devices. 2nd ed. Indianapolis, Ind: Cisco Press, 2008.

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Errington, Phillip Anthony. Application of neural network models to chromosome classification. Manchester: University of Manchester, 1995.

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Wichert, Terry S. Feature based neural network acoustic transient signal classification. Monterey, Calif: Naval Postgraduate School, 1993.

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Todd, Ian K. A new neural network algorithm for classification problems. [s.l: The author], 1999.

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Adamski, M. Design of Digital Systems and Devices. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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A modular and extensible network storage architecture. Cambrdige: Cambridge University Press, 1995.

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Varghese, George. Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices. Burlington: Elsevier, 2004.

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Network algorithmics: An interdisciplinary approach to designing fast networked devices. Amsterdam: Elsevier/Morgan Kaufmann, 2005.

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Book chapters on the topic "Classification of network devices"

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Khatun, Ferdousi, and Pratikshya Sharma. "Strahler Order Classification and Analysis of Drainage Network by Satellite Image Processing." In Advances in Communication, Devices and Networking, 915–22. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7901-6_98.

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Selver, M. Alper, and Cüneyt Güzeliş. "Multilevel Data Classification and Function Approximation Using Hierarchical Neural Networks." In Computational Methods for the Innovative Design of Electrical Devices, 147–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16225-1_8.

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Bassene, Avewe, and Bamba Gueye. "A Group-Based IoT Devices Classification Through Network Traffic Analysis Based on Machine Learning Approach." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 185–202. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70572-5_12.

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Giannou, Olympia, Anastasios D. Giannou, Dimitra E. Zazara, Dörte Kleinschmidt, Tobias Mummert, Björn Ole Stüben, Michael Gerhard Kaul, Gerhard Adam, Samuel Huber, and Georgios Pavlidis. "Liver Cancer Trait Detection and Classification Through Machine Learning on Smart Mobile Devices." In Proceedings of the International Neural Networks Society, 95–108. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_8.

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Najadat, Hassan, Maad Ebrahim, Mohammad Alsmirat, Obadah Shatnawi, Mohammed Nour Al-Rashdan, and Ahmad Al-Aiad. "Investigating the Classification of Human Recognition on Heterogeneous Devices Using Recurrent Neural Networks." In Sustainable and Energy Efficient Computing Paradigms for Society, 67–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51070-1_4.

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Rathinasabapathy, Ramadevi, Sheela Rani Balasubramaniam, Manoharan Narayanasamy, Prakash Vasudevan, Kalyasundaram Perumal, and Baldev Raj. "Classification of Pressure Drop Devices of Proto Type Fast Breeder Reactor through Seven Layered Feed Forward Neural Network." In Advances in Intelligent and Soft Computing, 157–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11282-9_17.

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Holman, Blake A., Joy Hauser, and George T. Amariucai. "Toward Home Area Network Hygiene: Device Classification and Intrusion Detection for Encrypted Communications." In Advances in Security, Networks, and Internet of Things, 195–209. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71017-0_14.

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Roopa, V., A. ChristyJeba Malar, R. Rekanivetha, R. Thanga Pradeep Kumar, R. Sarveshwaran, and A. Prithiksha Parameshwari. "Customized Music Classification and Recommendation System Based on Classifiers of Neural Networks and Sensor Embedded on Smart Devices." In Smart Computing Techniques and Applications, 805–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0878-0_79.

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Chowdhury, Dhiman Deb. "Timing Devices." In NextGen Network Synchronization, 65–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71179-5_5.

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Ramamurthy, Byrav. "Optical Network Devices." In Design of Optical WDM Networks, 9–31. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1675-0_2.

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Conference papers on the topic "Classification of network devices"

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Isuyama, Vivian Kimie, and Bruno De Carvalho Albertini. "Comparison of Convolutional Neural Network Models for Mobile Devices." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wperformance.2021.15724.

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In recent years mobile devices have become an important part of our daily lives and Deep Convolutional Neural Networks have been performing well in the task of image classification. Some considerations have to be made when running a Neural Network inside a mobile device such as computational complexity and storage size. In this paper, common architectures for image classification were analyzed to retrieve the values of accuracy rate, model complexity, memory usage, and inference time. Those values were compared and it was possible to show which architecture to choose from considering mobile restrictions.
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Payvar, Saman, Mir Khan, Rafael Stahl, Daniel Mueller-Gritschneder, and Jani Boutellier. "Neural Network-based Vehicle Image Classification for IoT Devices." In 2019 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2019. http://dx.doi.org/10.1109/sips47522.2019.9020464.

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Arora, Deepali, Kin Fun Li, and Alex Loffler. "Big Data Analytics for Classification of Network Enabled Devices." In 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2016. http://dx.doi.org/10.1109/waina.2016.131.

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Lelachaicharoeanpan, Jaroonwit, and Supachai Vongbunyong. "Classification of Surgical Devices with Artificial Neural Network Approach." In 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST). IEEE, 2021. http://dx.doi.org/10.1109/iceast52143.2021.9426258.

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Ceron, Joao M., Christian Scholten, Aiko Pras, and Jair Santanna. "MikroTik Devices Landscape, Realistic Honeypots, and Automated Attack Classification." In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2020. http://dx.doi.org/10.1109/noms47738.2020.9110336.

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Ibrahimi, Memedhe, Hatef Abdollahi, Alessandro Giusti, Cristina Rottondi, and Massimo Tornatore. "Machine Learning Regression vs. Classification for QoT Estimation of Unestablished Lightpaths." In Photonic Networks and Devices. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/networks.2020.nem3b.1.

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Chang Chen, Liangwei Cai, Yang Xiang, and Jun Li. "SwinTop: Optimizing memory efficiency of packet classification in network devices." In 2015 IEEE International Conference on Communication Software and Networks (ICCSN). IEEE, 2015. http://dx.doi.org/10.1109/iccsn.2015.7296139.

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Holm, Mikayle A., Alex Deakyne, Erik Gaasedelen, Weston Upchurch, and Paul A. Iaizzo. "Classification of Left Atrial Appendage Morphology Using Deep Learning." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9018.

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Abstract Atrial fibrillation, a common cardiac arrhythmia, can lead to blood clots in the left atrial appendage (LAA) of the heart, increasing the risk of stroke. Understanding the LAA morphology can indicate the likelihood of a blood clot. Therefore, a classification convolutional neural network was implemented to predict the LAA morphology. Using 2D images of 3D models created from MRI scans of fixed human hearts and a pre-trained network, an 8.7% error rate was achieved. The network can be improved with more data or expanded to classify the LAA from the automatically segmented DICOM datasets and measure the LAA ostia.
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Nazari, Najmeh, Seyed Ahmad Mirsalari, Sima Sinaei, Mostafa E. Salehi, and Masoud Daneshtalab. "Multi-level Binarized LSTM in EEG Classification for Wearable Devices." In 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2020. http://dx.doi.org/10.1109/pdp50117.2020.00033.

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Hosseini, Morteza, Hirenkumar Paneliya, Utteja Kallakuri, Mohit Khatwani, and Tinoosh Mohsenin. "Minimizing Classification Energy of Binarized Neural Network Inference for Wearable Devices." In 2019 20th International Symposium on Quality Electronic Design (ISQED). IEEE, 2019. http://dx.doi.org/10.1109/isqed.2019.8697574.

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Reports on the topic "Classification of network devices"

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Chakraborty, I., B. Kelley, B. Gallagher, and D. Merl. Performance Evaluation of Network Flow and Device Classification using Network Features and Device Embeddings. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1668490.

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Bradner, S., and J. McQuaid. Benchmarking Methodology for Network Interconnect Devices. RFC Editor, March 1999. http://dx.doi.org/10.17487/rfc2544.

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Bradner, S., and J. McQuaid. Benchmarking Methodology for Network Interconnect Devices. RFC Editor, May 1996. http://dx.doi.org/10.17487/rfc1944.

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Bradner, S. Benchmarking Terminology for Network Interconnection Devices. RFC Editor, July 1991. http://dx.doi.org/10.17487/rfc1242.

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Wilson, C. L., G. Candela, P. J. Grother, C. I. Watson, and R. A. Wilkinson. Massively parallel neural network fingerprint classification system. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4880.

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Popoviciu, C., A. Hamza, G. Van de Velde, and D. Dugatkin. IPv6 Benchmarking Methodology for Network Interconnect Devices. RFC Editor, May 2008. http://dx.doi.org/10.17487/rfc5180.

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Hawkins, Rupert S., K. F. Heideman, and Ira G. Smotroff. Cloud Data Set for Neural Network Classification Studies. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada256181.

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Wilson, Charles L., James L. Blue, and Omid M. Omidvar. Improving neural network performance for character and fingerprint classification by altering network dynamics. Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.ir.5695.

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Mu, Ruihui, and Xiaoqin Zeng. Improved Webpage Classification Technology Based on Feedforward Backpropagation Neural Network. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, September 2018. http://dx.doi.org/10.7546/crabs.2018.09.11.

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Grother, P. J. Comparison of FFT fingerprint filtering methods for neural network classification. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5493.

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