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2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2022

作者： Youssef, El-Nasser S. Labeau, Fabrice Kassouf, Marthe Alarie, Stéphane McGill University Department of Electrical and Computer Engineering MontrealQC Canada Hydro-Quebec's Research Institute Data Science and High Performance Computing Unit VarennesQC Canada

ISBN: (数字)9781665437752

ISBN: (纸本)9781665437752

The invasive deployment of digital technologies in power grids increases their exposure to cyber threats. In this paper, we study a type of load-altering cyberattack that targets peak shaving programs wherein a utility directly controls the loads of domestic electric water heaters. We consider a cyberattack scenario in which an adversary gains access to the advanced metering infrastructure and sends malicious activation commands to a large number of water heaters forcing them to simultaneously switch on in the midst of a peak shaving period, thus causing a sudden, large power demand that could lead to a system-wide blackout. We develop a simulation environment to simulate the loads of a population of electric water heaters under different operating conditions as well as under this type of cyberattack. Then, we propose a cybersecurity-by-design approach to mitigate the risk of such cyberattack. The efficacy of the proposed solution is analyzed using software experiments. © 2022 IEEE.

关键词： Cybersecurity

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Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention 4

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balassem, Zayd Srikanteswara, Ramya Esakki Madura, E. Anjaneyulu, Madugula Devi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science India Gokaraju Rangaraju Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector Machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed-established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM. © 2024 IEEE.

关键词： Smart power grids

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Detection of Military Vehicles based on Edge Devices from Unmanned Aerial Vehicle 2

Detection of Military Vehicles based on Edge Devices from Un...

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

作者： Umasankar, L. Anitha, D. Trisheela, S. Al-Jawahry, Hassan M. Borra, Subba Reddy S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Nitte Meenakshi Institute of Technology Dept of Cse Bengaluru India The Islamic University Najaf Iraq Computer Science and Engineering Maisammaguda Hyderabad India

ISBN: (纸本)9798350382952

The recent technology in surveillance for military vehicle parts forms the basis of intelligence warfare through information realized through monitoring and tracking operations in complex situations. Therefore, the process of identifying and classifying vehicles by an aerial vehicle installed with a resource-limited device and an intelligent object detection algorithm significantly assists security agencies. It is possible to make use of this vehicle either manually or autonomously. To overcome these issues, edge device based military vehicle detection from Unmanned Aerial Vehicle (UAV) is proposed. At present, no publicly available dataset exists that includes different military classes. Therefore, the proposed approach use a dataset consisting of 6772 images labeled as Tanks, Military Trucks, Helicopters, Aircraft, Civilian Cars and Civilian Aircraft. This data set is used to train deep learning models such as Quantized SSD Mobilenetv2 and Tiny Yolo v5 that are then compared with resource limited edge devices. The results reveal that Tiny Yolo v5 outperforms other models, showing high efficiency and suitability for edge-based devices due to its lightweight design. © 2024 IEEE.

关键词： Military vehicles

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Impact of Application Choice on iTracker Model Performance Evaluation: A Perspective 2

Impact of Application Choice on iTracker Model Performance E...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moghlebey, Adam Bou Haydar, Hady Moubayed, Abdallah Injadat, MohammadNoor School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan

ISBN: (纸本)9798350373363

Eye tracking is underpinned by the eye-mind hypothesis, which posits that individuals tend to direct their gaze toward the information they are currently cognitively engaged with. This aspect has garnered significant attention within deep learning research due to its versatile applications and research. The utilization of Artificial Intelligence (AI) in conjunction with webcams for tracking eye movements has gained increasing popularity in recent years and is anticipated to continue flourishing. This literature review aims to assess the current state and depth of the research in this domain and its various applications. We examine the different methods that employ eye tracking across a diverse range of applications, and by re-exploring existing research, we are able to filter out any overlooked potential or setbacks associated with the use of AI-webcam based systems, with the ultimate goal of maximizing their potential contributions to humanity. In our study, we analyze and evaluate a pioneering eye tracking algorithm, iTracker trained on a crowd-sourced data-set, GazeCapture within the context of the applications it may be used for. While we acknowledge inherent limitations, this widely adopted model forms a robust foundation for future refinements. Furthermore, our investigation encompasses diverse applications with varying accuracy requirements, making it invaluable for individuals seeking to integrate eye tracking into their projects. © 2023 IEEE.

关键词： Eye tracking

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Pivotal Auto-Encoder via Self-Normalizing ReLU

arXiv

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arXiv 2024年

作者： Goldenstein, Nelson Sulam, Jeremias Romano, Yaniv Department of Electrical and Computer Engineering The Technion Israel Biomedical Engineering Department The Mathematical Institute for Data Science Johns Hopkins University United States Department of Electrical and Computer Engineering The Department of Computer Science The Technion Israel

Sparse auto-encoders are useful for extracting low-dimensional representations from high-dimensional data. However, their performance degrades sharply when the input noise at test time differs from the noise employed during training. This limitation hinders the applicability of auto-encoders in real-world scenarios where the level of noise in the input is unpredictable. In this paper, we formalize single hidden layer sparse auto-encoders as a transform learning problem. Leveraging the transform modeling interpretation, we propose an optimization problem that leads to a predictive model invariant to the noise level at test time. In other words, the same pre-trained model is able to generalize to different noise levels. The proposed optimization algorithm, derived from the square root lasso, is translated into a new, computationally efficient auto-encoding architecture. After proving that our new method is invariant to the noise level, we evaluate our approach by training networks using the proposed architecture for denoising tasks. Our experimental results demonstrate that the trained models yield a significant improvement in stability against varying types of noise compared to commonly used architectures. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Survey of Covid Vaccine Hesitancy 15

Survey of Covid Vaccine Hesitancy

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

作者： Tripathi, Kirti Carter, Vivian Fergerson, An'Darius Prakash, Duraisamy Dutt, Varun Lloyd Institute of Engineering and Technology Electronics and Communication Engineering Uttar Pradesh India Tuskegee University Department of Psychology & Sociology AL United States University of Wisconsin-Green Bay Department of Computer Science WI United States Indian Institute of Technology Mandi School of Computing and Electrical Engineering Himachal Pradesh India

ISBN: (纸本)9798350370249

Vaccination serves as vital for strengthening immunity to illnesses and diseases that occurred amid the COVID-19 pandemic. There is a significant reluctance on the part of the general public as well as medical professionals to get vaccinated despite the affordability and availability of numerous licensed vaccines. This reluctance mainly causes worries about possible problems and negative effects. The keyword 'Vaccine hesitancy during COVID-19' was the primary search keyword on the Google Scholar platform. Seventeen studies that were published recently on the COVID19 pandemic that reported vaccine reluctance were found via our search, and these have been included in this review. During this survey, we found that the acceptance or rejection of vaccines was influenced by several criteria, such as income, education, gender, age, politics, religion, work position, and ethnicity. The vaccine was frequently refused for several reasons, which involve a general dislike of vaccinations, safety concerns or the belief that a rapidly developed vaccine may be too risky, the belief that immunity already exists or that the vaccine is unnecessary because COVID-19 is relatively harmless, a general lack of trust, doubts about the vaccine's efficacy, and distrust about the vaccine's origin. This review emphasizes the factors influencing COVID-19 vaccine acceptance and participation in clinical trials globally. It also presents strategies to enhance attitudes toward vaccination drives in the future. Additionally, governments and health authorities need to improve communication efforts and build public confidence. © 2024 IEEE.

关键词： Covid-19 Ethnicity Healthcare Vaccine Acceptance Vaccine Hesitancy

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Security in the World of Internet of Things (IoT): Overcoming Challenges

Security in the World of Internet of Things (IoT): Overcomin...

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2024 International Conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Gannavaram, Venkat Tulasi Krishna Desu, Sravya Jujjuru, Venkata Rohith Renikunta, Sri Vatsa Venkata Tejaswi Gajula, Arun Kumar Reddy, Ganta Raghotham School of Electrical Computer and Energy Engineering Arizona State University Tempe United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Department of Electronics and Communication Engineering Kakatiya Institute of Technology and Science Warangal India

ISBN: (纸本)9798350355338

Internet of Things (IoT) is turning out to be more prominent in our everyday existence, with smart home appliances and industrial control systems serving as examples. But the proliferation of IoT devices and internet connectivity increased the potential of security breaches. Botnet attacks, injection attacks, Domain Name Systems (DNS) spoofing attacks, firmware assaults, zero-day attacks, brute-force attacks, and cryptographic attacks may be used against IoT devices. These assaults may result in the loss of data, illegal access, and even physical damage to infrastructure and people. There are various potential courses of action. Machine Learning subpart of Artificial Intelligence have the capacity for finding and prevent dangers in real time. With blockchain technology, an IoT network can have a secure and unbreakable a record of every exchange and data transfers. Hardware-based security techniques, such as secure boot and trusted execution environments, may protect IoT devices against attacks that seek to compromise the underlying hardware or firmware. Standardizing security procedures may ensure that all devices can work together safely and achieve a given degree of security. The use of privacy-enhancing technology, IoT data may be kept confidential and sensitive information disguised. © 2024 IEEE.

关键词： Firmware

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data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis 2

Data-Driven with IoT Sensing and Deep Learning Model for Dyn...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, S. Mohan Thenmozhi, R. Balaji Damodhar, T.S. Epsiba, P. Malathi, N. Meenakshi, B. Cmr University Karnataka Bangalore India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chengalpattu India Madanapalle Institute of Technology & Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

This research introduces an innovative approach to continuously assess the condition of the skin barrier by combining advanced deep learning algorithms with Internet of Things (IoT) sensor technologies. For the system to function, it constantly uses IoT sensors to collect real-time data from various skin metrics, including moisture levels, pH balance, and environmental conditions. A complex analysis, made possible by deep learning, may be built using this vast amount of information. A type of deep learning technique that works well with data, recurrent neural networks (RNNs) are at the heart of this system. RNNs capture subtle changes over time in skin health by capitalizing on the temporal dependsequentialencies in skin data. Training RNNs iteratively on historical data covering a wide range of skin types and conditions allows them to detect patterns indicating healthy and damaged skin barriers. The proposed system has several benefits, such as individualized skincare suggestions, real-time information to guide adaptive skincare regimens, and continuous monitoring to identify skin concerns early. The system can discover complex patterns and correlations in the skin data, which results in better skincare solutions. © 2024 IEEE.

关键词： Recurrent neural networks

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Intrusion Detection System in Industrial Cyber-Physical System Using Clustered Federated Learning

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SN computer science 2023年第5期4卷 452页

作者： Jayagopal, Vellingiri Elangovan, Mohanraj Singaram, Saranya Sathasivam Shanmugam, Kavitha Bharathi Subramaniam, Balu Bhukya, Srinivasarao School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Artificial Intelligence and Data Science K.S.Rangasamy College of Technology Tamil Nadu Tiruchengode 637215 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Technology Kongu Engineering College Tamil Nadu Perundurai Erode 638060 India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamil Nadu Tiruchengode Namakkal 637205 India Department of Computer Science and Engineering Koneru Lakshmayya Education Foundation Andhra Pradesh Vaddeswaram 520002 India

The quick convergence in the industrial infrastructure with computing and networking has proliferated the attacks of industry-based Cyber-Physical Systems (CPS). Protecting such massive, sophisticated, and varied industrial CPSs from cyber-attacks is difficult. This research proposed a novel Clustered Federated Learning (CFL) technique that identifies cyber-attacks against industrial CPSs. A CFL architecture enables many industrial CPS to create an extensive model for maintaining privacy. The contribution of the research is to identify the intrusion in the CPS using CFL that is highly effective in terms of accuracy, error rate, and MCC. CFL is a Federated Multi-Task Learning (FMTL) paradigm that groups the attacks into clusters with simultaneously trainable data distributions by taking advantage of geometric features of the Federated Learning (FL) loss surface. Extensive tests on an industrial CPS dataset show their proposed CFL technique's high efficacy in identifying cyber-attacks on industrial CPSs and its advantages over cutting-edge solutions. Experiments are conducted on gas pipelining system-based datasets. In this research study, the data resource is split into two main categories: training is 80%, and testing is 20%. The proposed CFL method is effective by attaining the highest accuracy of 99.3%, the minimal error rate of 86%, and the highest Mathews Coefficient Correlation (MCC) of 0.7994 for communication round 10. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry 4 Learning Privacy Security Smart objects

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