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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

Computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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An Efficient Wireless Channel Estimation Model for Environment Sensing

arXiv

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arXiv 2024年

作者： Zaidi, Zainab R. Alpcan, Tansu Leckie, Christopher Erfani, Sarah Department of Electrical and Electronic Engineering The University of Melbourne Australia School of Computing and Information Systems The University of Melbourne Australia

This paper presents a novel and efficient wireless channel estimation scheme based on a tapped delay line (TDL) model of wireless signal propagation, where a data-driven machine learning approach is used to estimate the path delays and gains. The key motivation for our novel channel estimation model is to gain environment awareness, i.e., detecting changes in path delays and gains related to interesting objects and events in the field. The estimated channel state provides a more detailed measure to sense the field than the single-tap channel state indicator (CSI) in current OFDM systems. Advantages of this approach also include low computation time and training data requirements, making it suitable for environment awareness applications. We evaluate this model’s performance using Matlab’s ray-tracing tool under static and dynamic conditions for increased realism instead of the standard evaluation approaches that rely on classical statistical channel models. Our results show that our TDL-based model can accurately estimate the path delays and associated gains for a broad-range of locations and operating conditions. Root-mean-square estimation error was less than 10−4, or −40dB, for SNR ≥ 60dB in all of our experiments. Our results show that interference of a flying drone on signal multipaths, in a preliminary experiment, can be detected in estimated channel states which, otherwise, remains obscured in conventional CSI. © 2024, CC BY.

关键词： Machine learning

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Fused Spectatorship: Designing Bodily Experiences Where Spectators Become Players

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Proceedings of the ACM on Human-Computer Interaction 2023年第CHI PLAY期7卷 769–802页

作者： Patibanda, Rakesh Saini, Aryan Overdevest, Nathalie Montoya, Maria F. Li, Xiang Chen, Yuzheng Nisal, Shreyas Andres, Josh Knibbe, Jarrod Van Den Hoven, Elise Mueller, Florian Floyd Monash University Exertion Games Lab Department of Human-Centred Computing Melbourne3166 Australia University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom Xi'An Jiaotong-Liverpool University Suzhou China Australian National University School of Cybernetics Acton2601 Australia University of Melbourne School of Computing and Information Systems Parkville3010 Australia University of Technology Sydney Sydney Australia Eindhoven University of Technology Eindhoven Netherlands

Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called "Fused Spectatorship", where spectators watch their hands play games by loaning bodily control to a computational Electrical Muscle Stimulation (EMS) system. To showcase this concept, we designed three games where spectators loan control over both their hands to the EMS system and watch them play these competitive and collaborative games. A study with 12 participants suggested that participants could not distinguish if they were watching their hands play, or if they were playing the games themselves. We used our results to articulate four spectator experience themes and four fused spectator types, the behaviours they elicited and offer one design consideration to support each of these behaviours. We also discuss the ethical design considerations of our approach to help game designers create future fused spectatorship experiences. © 2023 ACM.

关键词： Watches

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Assessing Quality of Service Performance in SDN Controllers: POX vs. OpenDaylight

Assessing Quality of Service Performance in SDN Controllers:...

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Advancements in computing (ICAC), International Conference on

作者： Shashika Lokuliyana Pipuni Wijesiri Anuradha Jayakody Pradeep K.W. Abeygunawardhana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This research offers a comparative analysis of Qual-ity of Service (QoS) within Software Defined Networks (SDN) by assessing the POX and OpenDaylight controllers across various network topologies. The results show that OpenDaylight has a 32.59% higher average throughput than POX, demonstrating its superior data handling capabilities. Conversely, POX exhibits a 54.53% lower average delay and an 85.05% higher average jitter compared to OpenDaylight, highlighting its effectiveness in maintaining lower latency and variability in packet transmission times. These contrasting performances provide essential insights for network engineers in selecting the optimal SDN controller based on specific network demands, significantly influencing the overall network efficiency and stability.

关键词： Systematics Network topology Quality of service Jitter Throughput Real-time systems Stability analysis Delays Software defined networking Optimization

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Enhancing Agricultural Efficiency with IoT-Enabled Precision Irrigation Management Using LoRaWAN Technology

Enhancing Agricultural Efficiency with IoT-Enabled Precision...

引用

International Conference on Computational Intelligence and Communication Networks (CICN)

作者： J.A.S.K. Jayasinghe T.M.H.L. Thennakoon U.G.C. Ruchira N.G.C.L. Wimalasena J.A.D.C.A. Jayakody Dinithi Pandithage Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

The Internet of Things-empowered precision irrigation management system with LoRaWAN technology is presented given the growing food requirements across the world and pressing calls for judicious use of water in agriculture. Flow Pilot, specifically designed for chili farms, optimizes water application while enhancing crop health using precise and automated irrigation. The Flow Pilot features advanced sensors such as soil moisture, temperature, humidity, and rain sensors that collect real-time environmental data transmitted over a LoRaWAN network for analysis. Flow Pilot uses the Machine learning algorithm by analyzing sensor data to make localized irrigation recommendations based on crop needs, environmental factors, and weather forecasting. LoRaWAN gateways will support this for long-ranged communications over power-consuming agricultural fields. Besides this, it can use different network architectures. There is also an automated water tank management module that regulates the supply of water and pH levels to have optimal conditions for crop growth. A visually simple dashboard interface enables farmers to inspect and operate irrigation processes remotely, hence rendering the whole process far more efficient. Finally, over the extensive period of field tests executed by the project, Flow Pilot was able to save water, improve crop yields, and help support sustainable agriculture. For example, this testing supports the potential of IoT with LoRaWAN technologies, such as the Flow Pilot implementation, for a game change in conventional irrigation practices into a modern, scalable, and reliable solution.

关键词： Temperature sensors Water Irrigation Storage management LoRaWAN Machine learning Nutrients Real-time systems Sensor systems Crop yield

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Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

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SICE Journal of Control, Measurement, and System Integration 2021年第1期14卷 116-127页

作者： Omainska, Marco Yamauchi, Junya Beckers, Thomas Hatanaka, Takeshi Hirche, Sandra Fujita, Masayuki Department of Information Physics and Computing The University of Tokyo Tokyo Japan Department of Electrical and Systems Engineering University of Pennsylvania PennsylvaniaPA United States Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we propose an observer-based visual pursuit control integrating three-dimensional target motion learning by Gaussian Process Regression (GPR). We consider a situation where a visual sensor equipped rigid body pursuits a target rigid body whose velocity is unknown but dependent on the target's pose. We estimate the pose from visual information and propose a Gaussian Process (GP) model to predict the target velocity from the pose estimate. We analyse stability of the proposed control by showing that estimation and control errors are ultimately bounded with high probability. Finally, simulations illustrate the performance of the proposed control schemes even if the visual measurement is corrupted by noise. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Gaussian distribution

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Leveraging Artificial Intelligence to Secure Wireless Network: Exploring Threats, Existing Approaches, and Proposed Mitigation Strategies

Leveraging Artificial Intelligence to Secure Wireless Networ...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： Alice Lee Nan Xin Athirah Mohd Ramly Mehran Behjati Mhd Saeed Sharif Department of Computing and Information Systems Sunway University Selangor Malaysia School of Architecture Computing and Engineering UEL London United Kingdom Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

The exponential growth of network has introduced new Internet-of-Things (IoT) use cases that has enabling us convenience and comfort. The surge of IoT devices due to the capabilities brought by fifth generation (5G) have given rise to security threats and attacks, particularly malware attacks IoT botnets have been an alarming issue, where smart devices can be manipulated by malicious actors to commence subsequent attacks such as Denial of Service (DoS). Traditional and complex security techniques may not be a viable solution towards these resource-constrained devices with limited processing power. Machine Learning techniques (ML) are the rising trend, and it is often used in Intrusion Detection systems and Network Anomaly Detection. This paper emphasizes on analyzing and comparing various ML models on the IoT-23 dataset. It aims to predict anomalies and conclude the model with optimal performance and least computational time cost that can be used for network anomaly detection systems with real-time data in future works. The ML models used in this paper are Decision Trees (DT), K-nearest neighbours (KNN), Random Forest (RF), Naïve Bayes (NB) and Histogram Gradient Boosting (HGB). DT displayed the best performance with an accuracy score of 73% and F1 score of 0.49 with a time cost of 28.22 seconds.

关键词： Support vector machines Costs Accuracy 5G mobile communication Computational modeling Hardware Internet of Things Security Anomaly detection Tuning

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Adaptive Federated Learning for Efficient Network Traffic Management in Edge computing 4

Adaptive Federated Learning for Efficient Network Traffic Ma...

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4th International Conference on Ubiquitous computing and Intelligent Information systems, ICUIS 2024

作者： Babu, Kuricheti Suresh Venkatasubramanian, S. Kishore, S. Sree, K. Kavya Ramya Susanna, Ch.Lavanya Rao, Goda Srinivasa Lakkshmanan, Ajanthaa Pace Institute of Technology and Sciences Department of Computer Science and Engineering AndhraPradesh Valluru India Saranathan college of engineering Department of Computer Science and Business Systems Trichy India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Aditya University Department of Ai&ml Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Kl University Department of Cse Vaddeswaram Andhra Pradesh Guntur India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603 203 India

ISBN: (纸本)9798331529635

With the increasing prevalence of edge computing environment, there is a need to establish effective network traffic management that are able to manage vast data while also protecting the privacy of users. The proposed method enhances efficiency of bandwidth, reduce the network latency and manages computational demands without managing data privacy. This is achieved by utilizing the design of edge devices. The adaptable nature of the method enables it to react in real time to different traffic situations. It optimizes the distribution of resources and improves the overall performance of the network. Additionally, it can reduce the possibility of centralized data exposure, providing robust privacy preservation through the allowance of local model training on edge devices. Finally, this method is superior. The employment of federated learning to network traffic management provide efficiency to optimize the traffic in edge computing which is both scalable and safe. This makes it suitable for a wide variety of applications that are utilized in next generation networks © 2024 IEEE.

关键词： Federated learning

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Reinforcement Learning for Joint Resource and Power Allocation in D2D Communications

Reinforcement Learning for Joint Resource and Power Allocati...

引用

IEEE Conference on Decision and Control

作者： Ifrah Saeed Andrew C. Cullen Zainab R. Zaidi Sarah Erfani Tansu Alpcan Department of Electrical and Electronic Engineering The University of Melbourne Australia School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Device-to-Device (D2D) communication is critical in many public safety scenarios where access to network infrastructure is not guaranteed. In such out-of-coverage situations, random resource allocation is a straightforward way for direct communication by each D2D pair. However, such a resource allocation plan often results in severe interference and low throughput, which can degrade crucial communications performance. In contrast, the problem of centralised joint resource block selection and power control optimisation to maximise the total data rate of all D2D pairs, known as the sum-rate, is non-convex and NP-hard but can effectively reduce interference. To address the inability to solve this non-convex centralised problem setting due to limited information in resource-constrained out-of-coverage scenarios, we propose two distributed reinforcement learning schemes. Our methods allow D2D pairs to autonomously make decisions on their joint resource block and power allocation to minimise their mutual interference and maximise their sum-rate, while maintaining the quality of service constraints. To evaluate these methods in out-of-coverage scenarios, we conduct extensive performance evaluations using the ns-3 network simulator designed for public safety LTE-D2D. We demonstrate that our algorithms, in the absence of any centralised coordination and neighbouring information, autonomously reach time-averaged sum-rates that are within $98.2 \%$ and $98.6 \%$ of the rates achieved by the centralised optimisation solution.

关键词： Wireless communication Training Power control Interference Quality of service Reinforcement learning Public security Device-to-device communication Resource management Optimization

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Distributed and Localized Model-Predictive Control-Part I: Synthesis and Implementation

Distributed and Localized Model-Predictive Control-Part I: S...

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作者： Alonso, Carmen Amo Li, Jing Shuang Anderson, James Matni, Nikolai California Institute of Technology Department of Computing and Mathematical Sciences PasadenaCA91106 United States Columbia University Department of Electrical Engineering and the Data Science Institute New YorkNY10027 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

The increasing presence of large-scale distributed systems highlights the need for scalable control strategies where only local communication is required. Moreover, in safety-critical systems, it is imperative that such control strategies handle constraints in the presence of disturbances. In response to this need, in this article, we present the distributed and localized model-predictive control (DLMPC) algorithm for large-scale linear systems. DLMPC is a distributed closed-loop model-predictive control (MPC) scheme, wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We use the system-level synthesis framework to reformulate the centralized MPC problem and show that this allows us to naturally impose localized communication constraints between subcontrollers. The structure of the resulting problem can be exploited to develop an alternating-direction-method-of-multipliers-based algorithm that allows for a distributed and localized computation of closed-loop control policies. We demonstrate that the computational complexity of the subproblems solved by each subsystem in DLMPC is independent of the size of the global system. DLMPC is the first MPC algorithm that allows for the scalable computation and implementation of distributed closed-loop control policies and deals with additive disturbances. In our companion paper, we show that this approach enjoys recursive feasibility and asymptotic stability. © 2014 IEEE.

关键词： Model predictive control

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