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A Secure Federated Learning Framework for Residential Short-Term Load Forecasting

IEEE Transactions on Smart Grid 2024年第2期15卷 2044-2055页

作者： Husnoo, Muhammad Akbar Anwar, Adnan Hosseinzadeh, Nasser Islam, Shama Naz Mahmood, Abdun Naser Doss, Robin Deakin University Centre for Cyber Security Research and Innovation GeelongVIC3216 Australia Deakin University Centre for Smart Power and Energy Research GeelongVIC3216 Australia La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federated short-term load forecasting against Byzantine threats, we develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter's data while protect the security of FL models and architecture. Our proposed framework leverages the idea of gradient quantization through the Sign Stochastic Gradient Descent (SignSGD) algorithm, where the clients only transmit the 'sign' of the gradient to the control centre after local model training. As we highlight through our experiments involving benchmark neural networks with a set of Byzantine attack models, our proposed approach mitigates such threats quite effectively and thus outperforms conventional FedSGD models. © 2010-2012 IEEE.

关键词： Stochastic systems

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Perception System of Autonomous Vehicles: Sophisticated Attacks, Defense Strategies, and Future Directions 15

Perception System of Autonomous Vehicles: Sophisticated Atta...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neethu, M.R. Remyakrishnan, P. Mukesh, Sa Barath Kumar, J. Royal, Ala Manas Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore641112 India Amrita Vishwa Vidyapeetham TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore641112 India

ISBN: (纸本)9798350370249

The advent of autonomous vehicles marks a significant milestone in the evolution of automotive technologies. Central to the operation of autonomous vehicles is the perception system that interprets the surroundings and makes decisions. The security of perception systems against malicious attacks is a critical concern. This work thoroughly analyzes the vulnerabilities inherent in the perception systems of autonomous vehicles, examining the multifaceted threats posed by adversaries. The crucial need for ongoing research in AV security is underscored and a framework for future investigations to develop more secure and trustworthy autonomous vehicles is provided. The analysis emphasizes the importance of a proactive approach to security, advocating for integrating advanced defense mechanisms as a fundamental component of autonomous vehicle design and development and future research challenges. © 2024 IEEE.

关键词： Autonomous Vehicles Camera Lidar Perception Systems security attacks

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Investigating Labeled cyberbullying Incidents on the Weibo Social Network 21

Investigating Labeled Cyberbullying Incidents on the Weibo S...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Xu, Jun Yu, Songnan Lu, Xiaoyu Sean Wu, Di Kang, Qi School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210000 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9798350365221

This study aims to investigate cyberbullying incidents on Weibo by constructing a comprehensive dataset of labeled conversations. We collect 89K social media sessions from 10K user profiles and manually annotated the data to identify instances of cyberbullying. We analyze cyberbullying based on three fundamental characteristics, examining its distribution across different topics and the relationship between posting times and cyberbullying incidents. Additionally, we explore the influence of user popularity on the similarity of receiving bullying comments. Our study provides valuable insights into the characteristics of cyberbullying on Chinese social media and highlights the need for culturally sensitive detection models. © 2024 IEEE.

关键词： Phishing

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The Role of Blockchain technology in Supply chain Management for Data security 3

The Role of Blockchain Technology in Supply chain Management...

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3rd IEEE International Conference on Blockchain and Distributed Systems security, ICBDS 2024

作者： Birari, Dipika R. Rathod, Vijay U. Dandavate, Aarti Shelke, Nilesh Kumar, Ranjit Kulkarni, Masira M. S Army Institute of Technology Department of Information Technology Pune India G H Raisoni College of Engineering and Management Department of Artificial Intelligent and Machine Learning Wagholi Pune India Dhole Patil College of Engineering Department of Computer Engineering Pune India Symbiosis International Deemed University Symbiosis Institute of Technology Department of Computer Science and Engineering Nagpur Campus Pune India Ajeenkya D Y Patil University Department of Computer Engineering Pune India G H Raisoni College of Engineering and Management Department of Cyber Security and Data Science Wagholi Pune India

ISBN: (纸本)9798350354348

In a traditional supply chain system, the process of producing raw materials so that a product may be delivered to a customer is a manual operation with insufficient data and transaction security. The entire process is time consuming and requires a lot of effort. The undivided process is generally ineffective and unreliable for consumers. The data security, authenticity, the inclusion of will significantly improve the time management and transaction procedures of conventional supply chain management systems. Data is shielded from illegal access via the ground-breaking, decentralized technology known as blockchain. After smart contracts are put into place, the complete supply chain management (SCM) will be satisfied with the customer. When the mediator is contracted, which is possible in various ways, the plan becomes more reliable. This article places particular emphasis on lowering third party involvement in the supply chain system and enhancing data security. Conventional supply chain management techniques have several serious shortcomings. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. The reduction of third-party involvement in the supply chain system and the improvement of data security are the main goals of this article. This enhances process accessibility, effectiveness, and timeliness overall. They will feel safer throughout the payment procedure, which is the main benefit. Yet in this investigation, a smart contract and a peer-to-peer encrypted system were both used. There are a few additional features as well. © 2024 IEEE.

关键词： Smart contract

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Transforming cybersecurity in the Digital Era: The Power of AI 2

Transforming Cybersecurity in the Digital Era: The Power of ...

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2nd International Conference on cyber Resilience, ICCR 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Almajed, Rsha Alhasan, Waseem Salahat, Mohammed Lee, Jin Hie Ghazal, Taher M. International University For Science And Technology Computer And Information Technology Department Damascus Syria American University In The Emirates Department Of Computer Scienc Dubai United Arab Emirates School Of Computer Science And Informatics De Montfort University Dubai Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Miracle Clinic Seoul Korea Republic of Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

In the midst of the digital revolution of the 21st century, cybersecurity has come to be a primary social situation, requiring revolutionary and Innovative solution. To find the proper answer for this pressing demand, AI (AI) has developed as an innovative catalyst that has fundamentally transformed the cybersecurity landscape. The power of AI is exemplified by its ability to process and interpret large and heterogeneous cybersecurity data sets optimizing critical functions such as threat detection, asset prioritization, and vulnerability management beyond human capabilities swiftly and accurately. This transformation is redefining our cybersecurity approach. This paper presents a full exploration of AI's extreme influence on cybersecurity, delving deep into how AI tools not only enhance but often surpass human-mediated processes. By unraveling the intricacies of integrating AI into the realm of cybersecurity, we vividly illustrate AI's potential to anticipate, detect, and proactively mitigate cyber threats, empowering organizations to bolster their digital security. However, it's vital to recognize the essential difficulties of AI. We underline the critical necessity for non-stop human administration and involvement to confirm that cybersecurity processes are in proportion and powerful. Additionally, we mention the probable ethical concerns and underscore the importance of strong authority structures to accelerate accountable and transparent AI usage in cybersecurity. This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future. It additionally lays the base for current investigation and discourse on the role of AI in cybersecurity - a conversation of growing importance in our unexpectedly advancing digital age. © 2024 IEEE.

关键词： cybersecurity

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Simulating a Finite-State Markov Chain for Rayleigh Block-Fading Channel based on its Mathematical Model, and Practical Representation 25

Simulating a Finite-State Markov Chain for Rayleigh Block-Fa...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Attar, Hani Ababneh, Jafar Al-Hihi, Mohammad Rachini, Ali Solyman, Ahmed Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Erlangen Germany Department of Computer Science and Information Technology Jounieh Lebanon Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798331540012

In the proposed paper, simulating the Finite State Markov Chain (FSMC) to be implemented as Rayleigh Block-fading channel. Indeed, FSMC is regarded as a practical fading channel because of its random choice for future transmission probability. Accordingly, this paper introduces the FSMC technology with the theoretical limits and equations;moreover, the uplink and downlink Rayleigh Block-fading channel scenarios are also investigated and supported by the simulation set-up. The results for the Rayleigh fading channel for the Long-Term Evaluation-Advanced network were obtained as an example of the channel's attitude. The findings demonstrate that FSMC provides such reliable results and is suitable for use as a practical fading channel, particularly in terms of burst error behavior. © 2024 IEEE.

关键词： Markov chains

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Fault Classification based on Deep Learning for Photovoltaic Systems 2

Fault Classification based on Deep Learning for Photovoltaic...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kelin, A. R. Darshika Sudhakar, K. Surekha, Pothireddy Palanivel, R. Al-Jawahry, Hassan M. Karunya Institute of Technology and Sciences Data Science and Cyber Security Karunya Nagar Coimbatore India Department Electronics and Communication Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Telangana Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

In general, high power electronic converters such as Multilevel Inverters (MLI), that find their use in many applications, create difficulties of augmented switch count, capacitor flow current and reverse current flowing from inductive loads. above problems, in turn, result in faults, without which MLI reliability is unattainable. Therefore, fault classification and tolerance strategies are of paramount contribute to keeping reliability of inverters. Herein we present a deep learning (DL)-based fault classification approach for 15-level CHB Multi-level Inverter. proposed system includes pre-processing based on Adaptive Hilbert-Huang filtering, segmentation by a Partial Differential Equation (PDE), feature extraction utilizing Scale-Invariant Feature Transform (SIFT), and fault signal classification being performed by a Convolutional Neural Network (CNN) trained with a Honey Badger Optimization Algorithm (HBOA). MATLAB was applied to our method of identifying a set of four complex fault types for purpose of measurement. results of HBOA-optimized CNN have demonstrated highest accuracy rate of 99.91%. © 2024 IEEE.

关键词： Electric inverters

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Improving LSTM forecasting through ensemble learning: a comparative analysis of various models

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International Journal of Information technology (Singapore) 2024年第8期16卷 5113-5131页

作者： Ahmad, Zishan Shanmugasundaram, Vengadeswaran Biju Khan, Rashid Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam 686635 India LANOVIZ Security Solutions Kerala Ernakulam 682011 India

Supply chain management involves managing the entire manufacturing process, from purchasing supplies to delivering the final product. Demand forecasting helps businesses predict future customer demand by analyzing historical data and market patterns. While various papers discuss optimizing models, this research compares several machine learning models, such as ARIMA, SARIMA, and deep learning models like RNN, LSTM, GRU, and BLSTM. It also extends to approaches like ensemble learning with the LSTM model, discussing how ensemble learning can further improve the LSTM model. This paper explores ensemble learning in two ways: a) without model pruning, averaging all generated models, and b) with model pruning, removing underperforming models and averaging top performers. Experiments conducted on a public dataset from the University of Chicago achieved a very low RMSE loss of 9.26 on the LSTM model improved via ensemble learning with model pruning. This ensemble approach with model pruning improved accuracy in predicting future customer demand, and a complete pipeline integrating visualization and a notification system was developed. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ARIMA BLSTM Ensemble learning GRU LSTM RNN SARIMA

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User Behaviour based Insider Threat Detection using a Hybrid Learning Approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4573-4593页

作者： Singh, Malvika Mehtre, B.M. Sangeetha, S. Govindaraju, Venu Centre of Excellence in Cyber Security Institute for Development and Research in Banking Technology Established by Reserve bank of India Telangana Hyderabad500057 India Department of Computer Applications National Institute of Technology Trichy Tamil Nadu Tiruchirappalli620015 India Center for Unified Biometrics and Sensors Department of Computer Science and Engineering State University of New York at Buffalo New York BuffaloNY14260 United States

Insider threats constitute a major cause of security breaches in organizations. They are the employees/users of an organization, causing harm by performing any malicious activity. Most of the existing methods to detect insider threats are based on machine and deep learning and have the following limitations: they use predefined rules or stored signatures and fail to detect new or unknown threats;they require explicit feature engineering, which results in more false positives;they require a large amount of training data, and are computationally expensive. In this paper, an improved user behavior-based insider threat detection method is proposed using a hybrid learning approach that overcomes the above limitations. It uses bi-directional long-short-term memory for feature extraction, a feed-forward artificial neural network (using distance measurements) for feature selection, and a support vector machine for classification-normal user or malicious user. The genetic algorithm’s fast global search strategy is used for the support vector machine’s initial kernel selection. Finally, alerts are generated for each user based on their combined anomaly score. The proposed method is tested using the CMU-CERT r4.2 insider threat dataset, and its performance is evaluated using the following parameters: accuracy, precision, recall, f-measure, and area under curve-receiver operating characteristic curve. The results show a significant improvement over the existing methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Support vector machines

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