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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9781665464543

Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved detection capabilities using sophisticated techniques like deep neural networks. However, training models on extensive feature sets, such as multi-year data, can lead to confusion due to varied behavioral changes in electricity consumption. To address this, we propose a reformulation of the energy theft detection problem by segmenting the time series data and training individual models on each segment. These models’ anomaly scores are then aggregated to produce a final classification. Our framework has shown significant improvement, elevating the F1 score from 0.6 to 0.74, outperforming recent state-of-the-art techniques on the SGCC dataset, the only publicly available dataset labeled for energy theft. © 2024 IEEE.

关键词： Adversarial machine learning

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Battery-Induced Load Hiding and Its Utility Consequences 4

Battery-Induced Load Hiding and Its Utility Consequences

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Aly, Hussein Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah Qatar University College of Engineering Department of Computer Science and Engineering Doha Qatar Qatar University Kindi Center for Computing Research Doha Qatar

ISBN: (纸本)9798350306262

The introduction of smart grids allows utility providers to collect detailed data about consumers, which can be utilized to enhance grid efficiency and reliability. However, this data collection also raises privacy concerns. To protect user privacy, some studies suggest using battery-based load hiding. Nevertheless, the impact of widespread adoption of this approach on utility providers remain unclear. Our paper seeks to evaluate the effects of battery-based load hiding on two critical operations: user profiling and anomaly detection. Our findings reveal that the inclusion of battery users in datasets can diminish the quality of conclusions drawn from these data. This can result in a decrease in the Area Under the Curve (AUC) by more than 10% when attempting to profile users within single-occupant and multiple-occupant households. Furthermore, our experiments demonstrate that battery-based load hiding not only conceals information about users employing the batteries but can also lead to an increased rate of false positives for other non-battery users (from 0.15 to 0.37) within the system. To mitigate these adverse effects, our study assessed various mitigation strategies. In the context of user profiling, our experiment demonstrated that identifying and removing battery users from the analytical dataset using unsupervised detection methods can effectively lessen the impact of battery users. For anomaly detection, our experiment revealed that creating separate classification models for battery and non-battery users can significantly reduce the adverse influence of battery users on the detection performance. © 2024 IEEE.

关键词： Smart power grids

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Cluster-preserving sampling algorithm for large-scale graphs

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science China(Information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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Design of Methods for Encrypted Traffic Visualization 2

Design of Methods for Encrypted Traffic Visualization

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dalai, Chitaranjan Shaik, Nazeer Aruna Devi, K. Barve, Amit Patil, Harshal Taqui, Syed Noeman Odisha University Technology and Research Department of Civil Engineering Odisha Bhubaneswar751029 India Department of Computer Science and Engineering Bapatla522102 India Department of Computer Science PG Karnataka Bengaluru560 077 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat Waghodia391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350327533

As the prevalence of encrypted communication continues to rise, understanding and visualizing encrypted traffic have become paramount for network administrators, cybersecurity analysts, and researchers. This research study presents innovative methods for visualizing encrypted traffic patterns, leveraging diverse data sets representing various encrypted protocols. The goal of this research work is to enhance the comprehension of network behavior, identify anomalies, and strengthen cybersecurity measures without compromising the confidentiality of sensitive information. The goal is to develop techniques that can provide insights into encrypted traffic without requiring decryption. This research study discusses about different datasets of encrypted protocols and how they can be used to design new methods for visualizing the encrypted traffic. The main aim of this research work is to propose visualization methods that could help to reveal any interesting things detectable from encrypted traffic flows, especially HTTPS, SSH (Secure Shell), SMTP secured with TLS (Simple Mail Transfer Protocol) and DNS over HTTPS. © 2024 IEEE.

关键词： Decryption-free analysis Encrypted traffic Network monitoring SSL/TLS protocols Visualization techniques

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Enhancing the Zebra Optimization Algorithm with Chaotic Sinusoidal Map for Versatile Optimization

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第1期5卷 307-319页

作者： Anand, D. Khalaf, Osamah Ibrahim Chandra, G. Rajesh Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation GreenFileds A.P Vaddeswaram522302 India Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Department of Computer Science and Engineeirng KKR & KSR Institute of Technology and Sciences Andhra Pradesh India

In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA) is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibits great optimization capabilities, but the addition of CSM increases its potential even more. This addition greatly strengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks. CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions, including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM to investigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shown by CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatility through multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOA becomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficult optimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is a relatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership and following, in which members of the population—zebras in this case—cooperate to solve optimisation issues in the best possible ways. A chaotic sinusoidal map can improve the ZOA's exploitation and exploration capabilities, increasing its adaptability for optimisation across a range of problem *** success of CZOA and its potential as a potent © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Optimization

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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