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International Conference on Smart Communications and Networking (SmartNets)

作者： Ahmed T. El-Toukhy Islam Elgarhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Mohamed I. Ibrahem Fathi Amsaad Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Electrical Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Network and Computer Security: Cybersecurity SUNY Polytechnic Institute Utica NY USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Computer Science and Engineering Wright State University Dayton OH USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Smart meters (SMs) are deployed in smart power grids to monitor customer power consumption and facilitate energy management. However, fraudulent customers can compromise these SMs to manipulate power readings and engage in electricity theft cyber-attacks, resulting in reduced electricity bills. While various machine learning approaches have been employed for detecting such attacks, the potential of reinforcement learning (RL) remains unexplored. To bridge this gap, we propose a deep reinforcement learning (DRL) approach that leverages RL's adapt-ability to dynamic cyber-attacks and consumption patterns. This approach integrates exploration and exploitation mechanisms, enabling optimal decision-making. In this study, we present our approach in two scenarios. Firstly, we develop comprehensive detection models using deep Q networks (DQN) and double deep Q networks (DDQN) with various deep neural network architectures. Secondly, we address the challenges of defending against newly launched cyber-attacks. Extensive experimentation provides strong evidence of the effectiveness of our DRL approach in improving the detection of electricity theft cyber-attacks, as well as its capacity to efficiently adapt and defend against newly launched cyber-attacks.

关键词： Adaptation models Power demand Electricity Power system dynamics Detectors Deep reinforcement learning Smart meters

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Enhanced Area Triboelectric Nanogenerator Utilizing Recycled Single Used Plastic Bubble Wrap and Discarded Sketching Paper Ensuring Circularity of Material

Enhanced Area Triboelectric Nanogenerator Utilizing Recycled...

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Conference on Technologies for Sustainability (Sustech)

作者： Muhammad Umaid Bukhari Kashif Riaz Rehan Ahmed Adeel Arshad Khawaja Qasim Maqbool Arshad Khan Amine Bermak Abdelkrim Khelif Department of Computer and Software Engineering Information Technology University of the Punjab Lahore Pakistan Division of Computing and Information Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Electrical Engineering Information Technology University of the Punjab Lahore Pakistan Faculty of Engineering and Technology Superior University Lahore Pakistan Department of Computer Science Bahria University Lahore Pakistan

ISBN: (数字)9798331504311

ISBN: (纸本)9798331504328

Plastic pollution has become a major environmental challenge globally as millions of tons of plastic waste are entering oceans and landfills every year. The production and usage of plastic has surged due to its low cost and easy availability. This in turn leads to widespread usage of single-use plastics. Plastic waste takes centuries to degrade and when it decomposes, it breaks down into harmful microplastics that pollute the air, soil and water. This pollution of microplastics threatens wildlife, marine life and creates health risks for human beings. Moreover, plastic production and its waste release significant amounts of greenhouse gases which further contribute to climate change. As plastic waste is continuously accumulating in the environment its long-term effects on humans, climate and ecosystem are inevitable. On the other hand, the increased usage of electronic devices has transformed daily life and has raised concerns about the energy consumption of these devices and the generation of e-waste due to disposable batteries. Triboelectric nanogenerators (TENGs) have emerged as an innovative way to convert mechanical energy into electricity by utilizing frictional forces and vibrations. TENGs can be integrated into wearable electronics which in turn eliminates the need for frequent battery replacement and enhances sustainability. This study presents a low-cost triboelectric nanogenerator made from recycled bubble wrap and sketching paper (BW-TENG). BW-TENG offers an efficient and eco-friendly way to generate energy from common household waste materials. BW-TENG is capable of generating an open circuit voltage (V oc ) of 6.38 V, a short circuit current (I sc ) of 5.9 µA and a maximum output power of 7.84 µW respectively. BW TENG not only ensures the circularity of waste materials but also highlights the cost-effective manufacturing of a lightweight triboelectric nanogenerator.

关键词： Costs Nanogenerators Wildlife Production Water pollution Batteries Triboelectricity Sustainable development Power generation Plastic waste

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems Information management Monitoring Read only memory

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Image Reconstruction Improvement of Variable Coded Aperture Using Deep Learning Method for Gamma and Lensless Imaging Applications

Image Reconstruction Improvement of Variable Coded Aperture ...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Ariel Schwarz Amir Shemer Eliezer Danan Noa E. Cohen Yossef Danan Department of Electrical and Electronics Engineering Azrieli College of Engineering Jerusalem Israel Faculty of Engineering Bar-Ilan University Ramat-Gan Israel School of Software Engineering and Computer Science Azrieli College of Engineering Jerusalem Israel

ISBN: (纸本)9798350345995

In gamma ray imaging for nuclear medicine, coded aperture is used to improve sensitivity. one of the main reconstructing methods is inverse filtering (deconvolution), where the recorded image is cross-correlated with periodic inverse filter of coded array. The reconstruction is free of coding noise for arbitrary array. However, amplification of quantum noise affect the reconstructed image. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks. Statistically and experimentally pinhole arrays have at least one term which is zero, resulting unacceptably noisy reconstruction. In our previous research we presented new approach of variable coded aperture (VCA) design for far and near field imaging applications [1–4]. The imaging system is based on time multiplexing method using variable multi pinhole array. The unique variable design enables to overcome the spatial frequencies cutoff and small terms in the Fourier transform exists in static multi pinhole array. The overall pinholes positions are designed to avoid spatial frequencies loss. However, traces of duplications can still be detected in reconstruction using Wiener filtering. Furthermore, since coded aperture blocks most of the photons that enter the detector, the dynamic range of the image is limited, thus leading to low contrast image and inadequate colors gamut.

关键词： Deep learning Fourier transforms Image metrics Imaging systems Quantum noise Spatial frequency

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students

Enhancing Password Security: Analyzing Password Management P...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kando Kalman Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment.

关键词： Surveys Continuing education Power engineering Correlation engineering profession Education Passwords Complexity theory Security Protection

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Leveraging Bitcoin Mining Machines in Demand-Response Mechanisms to Mitigate Ramping-Induced Transients

arXiv

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arXiv 2024年

作者： Ginzburg-Ganz, Elinor Eyal, Ittay Machlev, Ram Baimel, Dmitry Santosh, Leena Belikov, Juri Levron, Yoash Segev, I. Keren, S. Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Computer Science Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Shamoon College of Engineering Beer-Sheva84100 Israel Pandit Deendayal Energy University India Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

We propose an extended demand response program, based on ancillary service for supplying flexible electricity demand. In our proposed scheme, we suggest a broader management model to control the scheduling and power consumption of Bitcoin mining machines. The main aspect that we focus on is suppressing the power ramping and related transient effects. We extend previous works on the subject, that study the impact of incorporating cryptocurrency mining machines into existing power grid, and explore the potential profit of exploiting this flexible load in the Israeli electricity market. We analyze a trend based on historical data, of increasing electricity prices and ramping costs due to the increasing penetration of renewable energy sources. We suggest an extension to the unit commitment problem from which we obtain the scheduling scheme of the Bitcoin mining machines. We use simulation and the real-world data acquired from the "Noga" grid operator to verify the proposed ancillary service and test its practical limits for reducing the ramping costs, under changing ratio of energy production from renewable sources. Out results suggests that the machine price and ratio of production from renewable sources plays a significant role in determining the profitability of the proposed demand-response program. © 2024, CC BY.

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Harnessing ML and NLP for Enhanced cybersecurity: A Comprehensive Approach for Phishing Email Detection

Harnessing ML and NLP for Enhanced Cybersecurity: A Comprehe...

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Information Technology (ACIT)

作者： Mahmoud AlJamal Rabee Alquran Mohammad Aljaidi Osama S AlJamal Ayoub Alsarhan Issa Al-Aiash Ghassan Samara Mohammad BaniSalman Mohammad Khouj Department of Cybersecurity Faculty of Science and Information Technology Irbid National University Irbid Jordan Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Al Hussein Technical University Amman Jordan Jordan Research and Training Reactor(JRTR) Irbid Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Emails stand as a predominant mode of online communication, facilitating the exchange of information. With the ubiquity of emails, there has been a marked rise in unsolicited messages, particularly phishing emails. These deceptive emails aim to mislead recipients, often masquerading as trustworthy entities, to extract confidential information. Embedded with dubious offers, advertisements, malicious links, malware, and trojans, perpetrators deploy these emails with malicious intent, primarily to commit fraud. Consequently, distinguishing between phishing attempts and genuine emails becomes paramount. This study constructs machine learning-based models to detect phishing emails, ensuring accurate discernment between genuine and fraudulent messages. Leveraging two datasets, the Fraud Email Dataset and the Phishing Email Dataset, we developed and evaluated classifiers, including Logistic Regression, Decision Tree, Random Forest, and SVM. The Random Forest model emerged as the most effective, achieving accuracies of 98.72% on the Fraud Email Dataset and 99.15% on the Phishing Email Dataset, demonstrating strong generalizability across diverse phishing patterns. These results underscore the efficacy of ensemble methods for enhancing cybersecurity by reliably detecting phishing emails.

关键词： Support vector machines Logistic regression Accuracy Phishing Electronic mail Fraud Trojan horses Reliability Information technology Random forests

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Weighted Density for The Win: Accurate Subspace Density Clustering

Weighted Density for The Win: Accurate Subspace Density Clus...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

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Multi-batch Nuclear-norm Adversarial Network for Unsupervised Domain Adaptation

Multi-batch Nuclear-norm Adversarial Network for Unsupervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Pei Wang Yun Yang Zhenyu Yu School of Information Science and Engineering Yunnan University Kunming China National Pilot School of Software Yunnan University Kunming China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China Guangzhou University Guangzhou China CISiP Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Adversarial learning has achieved great success for unsupervised domain adaptation (UDA). Existing adversarial UDA methods leverage the predicted discriminative information with Nuclear-norm Wasserstein discrepancy for feature alignment. However, the limited memory space makes it very difficult to accurately calculate the Nuclear-norm, which hinders domain adaptation. To address this challenge, we propose a multi-batch Nuclear-norm adversarial network, termed as MBAN. Specifically, we build a dynamic queue to cache features, which encourages to generate a large and consistent output matrix, enabling accurate calculation of the Nuclear-norm. Then, the multi-batch Nuclear-norm discrepancy is proposed, which can effectively improve the transferability and discriminability of the learned features. Experimental results show that MBAN could achieve significant performance improvement, especially when the number of categories is quite large. Code is available at https://***/peiwang0518/Multi-BAN.

关键词： Codes Accuracy Adversarial machine learning Noise measurement

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Everything you wanted to know about ChatGPT: Components, capabilities, applications, and opportunities

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Internet Technology Letters 2024年第6期7卷

作者： Heidari, Arash Navimipour, Nima Jafari Zeadally, Sherali Chamola, Vinay Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan College of Communication and Information University of Kentucky LexingtonKY United States Pilani India

Conversational Artificial Intelligence (AI) and Natural Language Processing have advanced significantly with the creation of a Generative Pre-trained Transformer (ChatGPT) by OpenAI. ChatGPT uses deep learning techniques like transformer architecture and self-attention mechanisms to replicate human speech and provide coherent and appropriate replies to the situation. The model mainly depends on the patterns discovered in the training data, which might result in incorrect or illogical conclusions. In the context of open-domain chats, we investigate the components, capabilities constraints, and potential applications of ChatGPT along with future opportunities. We begin by describing the components of ChatGPT followed by a definition of chatbots. We present a new taxonomy to classify them. Our taxonomy includes rule-based chatbots, retrieval-based chatbots, generative chatbots, and hybrid chatbots. Next, we describe the capabilities and constraints of ChatGPT. Finally, we present potential applications of ChatGPT and future research opportunities. The results showed that ChatGPT, a transformer-based chatbot model, utilizes encoders to produce coherent responses. © 2024 John Wiley & Sons Ltd.

关键词： Taxonomies

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