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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Enhancing Trustworthiness and Interoperability of Electronic Voting Systems through Blockchain Bridges

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第4期4卷 749-760页

作者： Rexha, Blerim Neziri, Vehbi Dervishi, Ramadan Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosovo10000 Department of Computer Science and Engineering UBT Higher Education Institution Prishtina Kosovo10000 Department of Computer Science Universum Higher Education Institution Prishtina Kosovo10000

Decentralized applications leveraging blockchain technology are gaining widespread adoption within the decentralized applications ecosystem. Interoperability, a fundamental concept facilitating seamless data and processing power exchange across diverse blockchain networks, is paramount in this context. The primary objective of this paper is to explore the transformative potential of "blockchain bridges" in facilitating secure and transparent electronic voting processes across multiple blockchain networks. The study employs a comprehensive analysis of various approaches, including atomic exchanges, sidechains, cross-chain bridges, token wrappers, and interledger protocols. The selection of a specific method is guided by the unique requirements and privacy considerations of the electronic voting use case. The application of two distinct blockchains serves as a practical demonstration, illustrating the principles of blockchain bridges in real-world scenarios. The research reveals that blockchain bridges not only streamline the exchange of data between diverse blockchain networks but also establish a dual decentralization paradigm. This paradigm enables the creation of openly maintained, purpose-specific, decentralized ledgers for electronic voting. The integration of blockchain bridges significantly reduces the risk of fraud, instilling greater confidence in the accuracy of election results. Thus, by presenting a comprehensive array of approaches and emphasizing their practical application, this research contributes to advancing the understanding and implementation of blockchain technology in the critical domain of electronic voting. © 2023, Ital Publication. All rights reserved.

关键词： Blockchain

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Design of an Automatic Price Tag System for Web-Based Retail Business 9

Design of an Automatic Price Tag System for Web-Based Retail...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Liawatimena, Suryadiputra Gunawan, Devina Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Jakarta11480 Indonesia Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper. © 2024 IEEE.

关键词： Sales

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CR-SAM: Curvature Regularized Sharpness-Aware Minimization 38

CR-SAM: Curvature Regularized Sharpness-Aware Minimization

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Wu, Tao Luo, Tie Wunsch, Donald C. Department of Computer Science Missouri University of Science and Technology United States Department of Electrical and Computer Engineering Missouri University of Science and Technology United States

ISBN: (纸本)1577358872

The capacity to generalize to future unseen data stands as one of the utmost crucial attributes of deep neural networks. Sharpness-Aware Minimization (SAM) aims to enhance the generalizability by minimizing worst-case loss using one-step gradient ascent as an approximation. However, as training progresses, the non-linearity of the loss landscape increases, rendering one-step gradient ascent less effective. On the other hand, multi-step gradient ascent will incur higher training cost. In this paper, we introduce a normalized Hessian trace to accurately measure the curvature of loss landscape on both training and test sets. In particular, to counter excessive non-linearity of loss landscape, we propose Curvature Regularized SAM (CR-SAM), integrating the normalized Hessian trace as a SAM regularizer. Additionally, we present an efficient way to compute the trace via finite differences with parallelism. Our theoretical analysis based on PAC-Bayes bounds establishes the regularizer’s efficacy in reducing generalization error. Empirical evaluation on CIFAR and ImageNet datasets shows that CR-SAM consistently enhances classification performance for ResNet and Vision Transformer (ViT) models across various datasets. Our code is available at https://***/TrustAIoT/CR-SAM. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Resource Allocation and Edge Computing for Dual Hop Communication in Satellite Assisted UAVs enabled VANETs

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 108-127页

作者： Hamdi, Mustafa Maad Rashid, Sami Abduljabbar Nafea, Ahmed Adil Department of Computer Science College of Computer Science and IT University of Anbar Ramadi Iraq Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Johor Batu Pahat86400 Malaysia Department of Artificial Intelligence College of Computer Science and IT University of Anbar Ramadi Iraq

VANETs are highly attractive and is used in maximum of the applications of cross-regional communication. To increase the coverage of the vehicular network, Unmanned Arial Vehicles (UAVs) are introduced, and they get connected with the satellite networks to perform heterogeneous communication. With the help of this connectivity, the communication quality of ground level to air medium is increased. Currently the vehicle usage is highly increased and as a results of communication link failure, improper resource allocation are arises whither abruptly assumes a stability about a network with that increases an energy consumption and communication delay in the heterogeneous networks. In these conditions, thus study is idea of Resource Allocation and Edge Computing for Dual Hop Communication (RAEDH) in introduced in satellite assisted UAVs enabled VANETs. The major sections of the approach are UAV assisted mobile computing, resource allocation among the vehicles and the UAVs, and dual communication among the vehicles and the *** these methods the input resources are properly allocated and that reduces the power utility and communication delay. Initially, the vehicular network is established, incorporating trusted components like TA, RSU, and CRS. Subsequently, mobile edge computing reduces energy consumption through computation offloading and optimized UAV trajectory selection. Resource allocation, facilitated by whale optimization, ensures effective utilization across vehicles. The implementation of this method is done in NS3, and the scenario is analyzed using two parameters like number of vehicles and its speed. The output parameters that remain thought-out over a performance examination stay throughput, end-to-end delay, energy efficiency, packet loss, packet delivery ratio, and routing overhead, and as well those results are compared with the earlier methods. Finally, dual-hop transmission between vehicles and UAVs enhances delivery ratio and throughput. From

关键词： Satellites

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Shrink: Data Compression by Semantic Extraction and Residuals Encoding

Shrink: Data Compression by Semantic Extraction and Residual...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Sun, Guoyou Karras, Panagiotis Zhang, Qi Aarhus University Digit Department of Electrical and Computer Engineering Denmark Aarhus University Digit Department of Computer Science Denmark University of Copenhagen Department of Computer Science Denmark

ISBN: (纸本)9798350362480

The distributed data infrastructure in Internet of Things (IoT) ecosystems requires efficient data-series compression methods, as well as the capability to meet different accuracy demands. However, the compression performance of existing compression methods degrades sharply when calling for ultra-accurate data recovery. In this paper, we introduce Shrink, a novel highly accurate data compression method that offers a higher compression ratio and lower runtime than prior compressors. Shrink extracts data semantics in the form of linear segments to construct a compact knowledge base, using a dynamic error threshold which can adapt to data characteristics. Then, it captures the remaining data details as residuals to support lossy compression at diverse resolutions as well as lossless compression. As Shrink effectively identifies repeated semantics, its compression ratio increases with data size. Our experimental evaluation demonstrates that Shrink outperforms state-of-art methods, achieving a twofold to fivefold improvement in compression ratio depending on the dataset. © 2024 IEEE.

关键词： Data accuracy

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Stability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networks 63

Stability and Performance Analysis of Discrete-Time ReLU Rec...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Noori, Sahel Vahedi Hu, Bin Dullerud, Geir Seiler, Peter University of Michigan Department of Electrical Engineering & Computer Science United States University of Illinois Department of Electrical and Computer Engineering Urbana-Champaign United States University of Illinois Department of Mechanical Science and Engineering Urbana-Champaign United States

ISBN: (纸本)9798350316339

This paper presents sufficient conditions for the stability and 2-gain performance of recurrent neural networks (RNNs) with ReLU activation functions. These conditions are derived by combining Lyapunov/dissipativity theory with Quadratic Constraints (QCs) satisfied by repeated ReLUs. We write a general class of QCs for repeated ReLUs using known properties for the scalar ReLU. Our stability and performance condition uses these QCs along with a 'lifted' representation for the ReLU RNN. We show that the positive homogeneity property satisfied by a scalar ReLU does not expand the class of QCs for the repeated ReLU. We present examples to demonstrate the stability / performance condition and study the effect of the lifting horizon. © 2024 IEEE.

关键词： Recurrent neural networks

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HDMTK: Full Integration of Hierarchical Decision-Making and Tactical Knowledge in Multi-Agent Adversarial Games

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 465-479页

作者： Li, Wei Hu, Boling Song, Aiguo Huang, Kaizhu Southeast University School of Instrument Science and Engineering Jiangsu Nanjing210096 China Duke Kunshan University Department of Electrical and Computer Engineering Jiangsu Kunshan215316 China

In the field of adversarial games, existing decision-making algorithms primarily rely on reinforcement learning, which can theoretically adapt to diverse scenarios through trial and error. However, these algorithms often face the challenges of low effectiveness and slow convergence in complex wargame environments. Inspired by how human commanders make decisions, this paper proposes a novel method named Fully Integrating Hierarchical Decision-Making and Tactical Knowledge (HDMTK). This method comprises an upper reinforcement learning module and a lower multi-agent reinforcement learning module. To enable agents to efficiently learn the cooperative strategy, in HDMTK, we separate the whole task into explainable subtasks, and design their corresponding subgoals for shaping the online rewards upon tactical knowledge. Experimental results on the wargame simulation platform 'MiaoSuan' show that, compared to the advanced multi-agent reinforcement learning methods, HDMTK exhibits superior performance and faster convergence in the complex scenarios. © 2016 IEEE.

关键词： Adversarial machine learning

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A Case Study on the Convergence of Direct Policy Search for Linear Quadratic Gaussian Control

A Case Study on the Convergence of Direct Policy Search for ...

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2024 American Control Conference, ACC 2024

作者： Keivan, Darioush Seiler, Peter Dullerud, Geir Bin, H. University of Illinois at Urbana-Champaign Department of Mechanical Science & Engineering United States University of Michigan Department of Electrical Engineering and Computer Science United States University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350382655

Policy optimization has gained renewed attention from the control community, serving as a pivotal link between control theory and reinforcement learning. In the past few years, the global convergence theory of direct policy search on state-feedback linear control benchmarks has been developed. However, it remains difficult to establish the global convergence of policy optimization on the linear quadratic Gaussian (LQG) problem, marked by the presence of suboptimal stationary points and the lack of cost coerciveness. In this paper, we revisit the policy optimization intricacies of LQG via a case study on first-order single-input single-output (SISO) systems. For this case study, while the issue related to suboptimal stationary points can be easily fixed via parameterizing the policy class more carefully, the non-coerciveness of the LQG cost function still poses a substantial obstacle to a straightforward global convergence proof for the policy gradient method. Our contribution, within the scope of this case study, introduces an approach to construct a positive invariant set for the policy gradient flow, addressing the non-coerciveness issue in the global convergence proof. Based on our analysis, the policy gradient flow can be guaranteed to converge to the globally optimal full-order dynamic controller in this particular scenario. In summary, although centered on a specific case study, our work broadens the comprehension of how the absence of coerciveness impacts LQG policy optimization, highlighting inherent complexities. © 2024 AACC.

关键词： Cost functions

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