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Accurate threat hunting in industrial internet of things edge devices

Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting edge devices Multi-class anomalies Ensemble methods

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A Neural Network-Based Trust Management System for edge devices in Peer-to-Peer Networks

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Computers, Materials & Continua 2019年第6期59卷 805-815页

作者： Alanoud Alhussain Heba Kurdi Lina Altoaimy Computer Science Department King Saud UniversityRiyadh11451Saudi Arabia Information Technology Department King Saud UniversityRiyadh11451Saudi Arabia

edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P *** P2P networks ensures scalability and removes the need for centralized ***,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s *** compromises users’safety and makes them lose their confidence about the files or services they are *** address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is *** this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in *** experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.

关键词： Trust management neural networks peer to peer machine learning edge devices

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Optimized Energy Efficient Strategy for Data Reduction Between edge devices in Cloud-IoT

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Computers, Materials & Continua 2022年第7期72卷 125-140页

作者： Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Brainware UniversityBarasat700125India Department of Computer Science and Engineering Global Institute of Management and TechnologyKrishnanagar741102India Department of Information Technology School of Computing SciencesVels Institute of ScienceTechnology&Advanced StudiesChennai600117India School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short *** growing energy usage related to the migration of data into the cloud is one of the biggest *** computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of ***,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge *** the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT *** initial stage is followed by the reconstructing and the processing *** processed data is transmitted to the nodes through controlled deep learning *** entire stage of data collection,transmission and data reduction between edge devices uses less *** experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive *** consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES *** accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

关键词： Energy efficient internet of things transmission performance cloud computing edge devices

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A review on edge analytics:Issues,challenges,opportunities,promises,future directions,and applications

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Digital Communications and Networks 2024年第3期10卷 783-804页

作者： Sabuzima Nayak Ripon Patgiri Lilapati Waikhom Arif Ahmed National Institute of Technology Silchar India Ericsson Sweden

edge technology aims to bring cloud resources(specifically,the computation,storage,and network)to the closed proximity of the edge devices,i.e.,smart devices where the data are produced and *** computing and application in edge devices lead to emerging of two new concepts in edge technology:edge computing and edge *** analytics uses some techniques or algorithms to analyse the data generated by the edge *** the emerging of edge analytics,the edge devices have become a complete ***,edge analytics is unable to provide full support to the analytic *** edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply,small memory size,limited resources,*** article aims to provide a detailed discussion on edge *** key contributions of the paper are as follows-a clear explanation to distinguish between the three concepts of edge technology:edge devices,edge computing,and edge analytics,along with their *** addition,the article discusses the implementation of edge analytics to solve many problems and applications in various areas such as retail,agriculture,industry,and ***,the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues,emerging challenges,research opportunities and their directions,and applications.

关键词： edge analytics edge computing edge devices Big data Sensor Artificial intelligence Machine learning Smart technology Healthcare

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Intelligent indoor metasurface robotics

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National Science Review 2023年第8期10卷 20-32页

作者： Hanting Zhao Shengguo Hu Hongrui Zhang Zhuo Wang Hao Dong Philipp del Hougne Tie Jun Cui Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Center on Frontiers of Computing Studies School of Computer Science Peking University The French National Centre for Scientific Research(CNRS) University of Rennes 4. State Key Laboratory of Millimeter WavesSoutheast University

Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics(I2MR),where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR’s limbs(motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR’s brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR’s brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ～2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.

关键词： intelligent metasurfaces intelligent robotics edge devices IoT 6G

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