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CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST)

作者： Sadra Galavani Soheil Mahdizadeh Mohammad Pourashory Amir Mahdi Rasouli Mohsen Ansari Department of Computer Engineering Cyber-Physical Systems Laboratory (CPSLab) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331529284

ISBN: (纸本)9798331529291

Reducing latency in the Internet of Things (IoT) remains a critical concern. While cloud computing facilitates communication, it falls short of meeting real-time requirements reliably. Edge and fog computing have emerged as viable solutions by positioning computing nodes closer to end users, offering lower latency and increased processing power. An edge-fog framework comprises various components, including edge and fog nodes, whose strategic placement is crucial as it directly impacts latency and system cost. This paper presents an effective and tunable node placement strategy based on a genetic algorithm to address the optimization problem of deploying edge and fog nodes. The task assignment for this algorithm involves two key components: first, it identifies the nearest node to a given user; second, it assesses the node’s capacity to ensure it has sufficient frequency resources to handle the assigned tasks. The ultimate objective is to minimize latency and cost through optimal node placement. Simulation results demonstrate the proposed framework achieves up to 8.4% latency and 65.2% cost reduction.

关键词： Costs Heuristic algorithms Simulation Refining Real-time systems Internet of Things Reliability Optimization Genetic algorithms Edge computing

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Efficient Segmentation Approach for Different Medical Image Modalities

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computers, Materials & Continua 2022年第11期73卷 3119-3135页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical *** paper provides a solid introduction to image enhancement along with image segmentation *** the first step,the morphological operations are employed to ensure image detail protection and *** objective of using morphological operations is to remove the defects in the texture of the ***,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster *** proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster *** is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition *** results are performed on different medical image ***(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this *** obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image *** results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PET MRI morphological operations FCM active contours

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Cryptocurrency-Enabled Energy Trading in Micro-Grid Using Blockchain technology

Cryptocurrency-Enabled Energy Trading in Micro-Grid Using Bl...

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cyber Resilience (ICCR), International Conference on

作者： Ahsan Waseem Mohd Bilal Abhishek Bhardwaj M Saad Bin Arif Taher M. Ghazal Department of Electrical Engineering Aligarh Muslim University Aligarh India Computer Science Department Faculty of Information Science and Technology Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This article explores energy trading in grid-connected microgrids powered by renewable sources, utilizing cryptocurrency and blockchain technology. Through net metering, it emphasizes the proactive roles of prosumers and consumers in managing excess energy. The study investigates peer-to-peer energy transfers with blockchain-based cryptocurrencies, high-lighting their advantages in affordability, trade efficiency, and openness. Real-world case like LO3 Energy and Power Ledger are examined to address regulation, scalability, and cybersecurity concerns. The paper explores blockchain-enabled energy trade revolutionizing the industry, enhancing community resilience, accelerating renewable energy adoption, and reducing reliance on outdated technologies.

关键词： Renewable energy sources Shape Scalability Smart contracts Regulation Blockchains Cryptocurrency

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Rapid Autonomy Transfer in Reinforcement Learning with a Single Pre- Trained Critic 36

Rapid Autonomy Transfer in Reinforcement Learning with a Sin...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Karim, Muhammad Faraz Deng, Yunjie Niu, Luyao Ramasubramanian, Bhaskar Alexiou, Michail Sahabandu, Dinuka Poovendran, Radha Mertoguno, Sukarno Georgia Institute of Technology AtlantaGA United States University of Washington Network Security Lab Department of Electrical and Computer Engineering SeattleWA United States Western Washington University Electrical and Computer Engineering BellinghamWA United States Kennesaw State University MariettaGA United States

ISBN: (纸本)9798331527235

Reinforcement learning (RL) is a well-studied framework to solve complex decision-making problems in unknown environments. The actor-critic model in RL facilitates autonomy transfer by allowing agents to iteratively update their policies using ongoing dynamic evaluations of value functions via a critic. In this paper, we examine the impact of using different pretrained critics on the performance of actor-critic algorithms. First, in any single given environment, we show that a pretrained critic can be effective in reducing the duration of an initial training phase, thereby accelerating convergence by a factor of up to 2×. In this setting, we identify the critical range of the number of episodes for which a critic will need to be trained in order for it to be an effective pretrained critic. Second, we show that a critic trained in one environment enables transfer of autonomy by aiding learning of behaviors in a different, yet related environment. We carry out extensive experiments on a bipedal locomotion task in the MuJoCo physics engine to verify our hypotheses. Our results in this paper mark the first step towards demonstrating the role and impact of pretrained critics to achieve rapid autonomy transfer for complex reinforcement learning tasks while minimizing costs associated with retraining in new environments. © 2024 IEEE.

关键词： Reinforcement learning

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An Improved Neural Classification Logic to Predict Kidney Disease Identification Using Medical Image Processing Principles

An Improved Neural Classification Logic to Predict Kidney Di...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Balaji, C.G. Balachandran, G. Imran, Md. Ujwala, S.Vamsi Edlabadkar, Ajinkya P. Fawareh, Hamed Department of Digital and Telecom Management Lavale Maharashtra Pune412115 India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam Andhra Pradesh PrakasamDistrict523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Kidney disease is a significant global health concern, requiring accurate and early diagnosis to prevent progression and ensure timely treatment. This study proposes an improved neural classification logic combining an AutoEncoder and an Artificial Neural Network (ANN) to predict kidney disease using medical image processing techniques. A dataset comprising kidney images from CT, MRI, and ultrasound was utilized to train and test the model. The AutoEncoder was employed for unsupervised feature extraction, reducing dimensionality while retaining key information related to kidney abnormalities. The ANN then classified these features into healthy or diseased categories. Image preprocessing techniques such as noise reduction, resizing, normalization, and contrast enhancement were applied to ensure data consistency. Additionally, advanced segmentation algorithms like U-Net were used to isolate the kidney regions from the background, focusing on the Regions of Interest (ROI). Data augmentation was applied to improve generalization. The proposed model achieved a classification accuracy of 94.8%, outperforming baseline models such as CNN (92.1 %) and SVM (88.7%). The AutoEncoder + ANN combination demonstrated superior precision, recall, and Fl-score, with an AUC-ROC of 97.4%. The results highlight the model's robustness and potential for practical applications in the early detection of kidney disease. © 2024 IEEE.

关键词： Noise abatement

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Hybrid Segmentation Approach for Different Medical Image Modalities

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computers, Materials & Continua 2022年第11期73卷 3454-3471页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The segmentation process requires separating the image region into sub-regions of similar *** sub-region has a group of pixels having the same characteristics,such as texture or *** paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)*** extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation *** existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed *** paper extensively analyzes and summarizes the comparative investigation of these ***,a prediction of the improvement involves the combination of these techniques is *** obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

关键词： Image segmentation ultrasonic images X-ray images CT images PET images MR images fuzzy c-mean morphological operations active contour

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A Hybird Alignment Loss for Temporal Moment Localization with Natural Language

A Hybird Alignment Loss for Temporal Moment Localization wit...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Guo, Chao Liu, Daizong Zhou, Pan The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Electronic Information and Communication Huazhong University of Science and Technology China Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

This paper addresses the problem of temporal moment localization with natural language, which aims to localize a target video moment according to a language description. A key challenge in this task is how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Traditional methods generally utilize the vanilla attention mechanism to simulate soft alignment between extracted video and sentence features, however, they lack the training signal to alleviate the domain gap between video and sentence domains. In this paper, we propose a novel cross-domain alignment module to deeply align the features in two domains, yielding to learn better representations. We evaluate our model on three public datasets: Charades-STA, ActivityNet Captions and TACoS, and the experiments prove that our proposed cross-domain alignment module can bring significant improvement, and can be also applied to other baseline models. © 2022 IEEE.

关键词： Alignment

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Image-Based Malware Classification: A Systematic Literature Review

Image-Based Malware Classification: A Systematic Literature ...

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Cryptography, Informatics, and cybersecurity (ICoCICs), IEEE International Conference on

作者： Yudhistira Dimas Wahyu Utomo Charles Lim Cyber Security Engineering Cyber and Crypto Polytechnic Bogor Indonesia Information Technology Department Swiss German University Tangerang Banten Indonesia

Malware, a shortened term for malicious software, refers to software or code designed to harm targets using malicious activities. Effective classification of malware is essential for developing robust detection and prevention mechanisms. This study presents a systematic review of the literature on image-based malware classification. The research methodology employed the DICARe technique. The results and discussion section revealed that 31 papers met the criteria and were highly quality. The study also examined the distribution of research on image-based malware classification by country of origin, with contributions from 15 different countries. Various classification techniques were identified in the literature, indicating the continuous development and evolution of solutions in this area. The findings suggest increased research output due to the growing importance of cybersecurity and increased funding for research in this field. This study provides valuable insights into the current state of image-based malware classification research and highlights the need for further advancements in this area.

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Actuation Sequence Tampering Attack Detection on Field-Level Micro-Electrode Dot Arrays Based on 2-Layer Authentication 23

Actuation Sequence Tampering Attack Detection on Field-Level...

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Proceedings of the 2023 13th International Conference on Communication and Network security

作者： Xiaodong Guo Chenxi Lyu Zhenyi Chen Xiaoding Wang Ruishen Bao Chen Dong College of Computer and Big Data Fuzhou University China Department of Computer Science and Engineering University of South Florida USA College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9798400707964

As a next-generation digital microfluidic biochip (DMFB) platform, micro-electrode-dot -array (MEDA) play an important role in the biochemical industry with more advanced fluidic manipulation and higher reliability. As the MEDA biochip market has grown by leaps and bounds, there are still some issues with MEDA that have not kept pace in terms of security. One of them is that an attacker of a MEDA biochip can change the protocol parameters by tampering with the actuation sequence (AS) in the field before performing the bioassay, which can lead to catastrophic consequences. The attackers could be untrustworthy third parties introduced by the distributed supply chain and malicious remote parties at the time of cyber-physical MEDA biochip deployment. In this paper, a two-layer authentication solution for AS tampering attack detection is proposed, based on SHA-256 hash function to ensure the security of the authentication process and at the same time, it is able to detect and locate the tampered AS with simplicity, security and efficiency. Experiments demonstrate the obvious advantages in security, performance and applicability within a controllable time complexity.

关键词： actuation sequence micro-electrode-dot-array security tempering attack

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Enhancing Side-Channel Attacks Prediction using Convolutional Neural Networks

Enhancing Side-Channel Attacks Prediction using Convolutiona...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Khalid Alemerien Sadeq Al-Suhemat Fadi Alsuhimat Enshirah Altarawneh Information Technology Department Tafila Technical University Tafila Jordan Faculty of Information Technology Isra University Amman Jordan Cyber Security Department Philadelphia University Amman Jordan Computer Engineering Department Hashemite University Zarqa Jordan

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

A common type of cyberattack is the side channel attack (SCA), which affects many devices and equipment connected to a network. These attacks have different types, like power attacks such as DPA, electromagnetic attacks, storage attacks, and others. Researchers and information security experts are concerned about SCA targeting devices, as they can lead to the loss and theft of important information. Using deep learning (DL) techniques in SCA analysis can be an efficient tool for detecting the SCA. Many previous works have tried to carry out the SCA in order to mitigate the impact of these attacks, but they encountered difficulties in detecting the SCA, whether in selecting the suitable dataset or applying the most efficient machine learning or deep learning techniques for achieving high performance. Therefore, we developed in this paper a deep learning-based model to detect SCAs using a dataset related to power attacks (the DPAv4 dataset) and the Convolutional Neural Networks (CNN) algorithm to train and test the DPAv4 dataset. The findings of our experiments showed a significant improvement in the performance of DL-based techniques in the detection of SCA. The proposed CNN-based model achieved an accuracy of 0.814 in detecting SCA and a loss rate of 0.581.

关键词： Deep learning Machine learning algorithms Computational modeling Information security Side-channel attacks Prediction algorithms Convolutional neural networks Information technology Electromagnetics cyberattack

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