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Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing

International Journal of Networking and Virtual Organisations 2024年第3期31卷 169-190页

作者： Sundara Rajan, R. Kirithiga Nandini, G. Lin, Yuqing Reji, Remi Mariam Department of Mathematics Hindustan Institute of Technology and Science Chennai603 103 India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai603 103 India School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Department of Mathematics Christian College Kerala Chengannur689 122 India

A system’s fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network’s fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Fault tolerant computer systems

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Zero-day Android botnet detection using neural networks

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Neural computing and Applications 2024年 1-11页

作者： Seraj, Saeed Pimenidis, Elias Trovati, Marcello Polatidis, Nikolaos Department of Computer Science Royal Holloway University EghamTW20 0EX United Kingdom School of Computing and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. © The Author(s) 2024.

关键词： Bot (Internet)

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FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xiao, Bingnan Zhang, Jingjing Ni, Wei Wang, Xin Fudan University School of Information Science and Technology Department of Communication Science and Engineering Shanghai200433 China CSIRO Data61 MarsfieldNSW2122 Australia School of Computing Science and Engineering KenningtonNSW2052 Australia

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adjust their individual learning rates and local training iterations, adapting to their instantaneous computing powers. The convergence upper bound of FLARE is established rigorously under a general setting with non-convex models in the presence of non-i.i.d. datasets and imbalanced computing powers. By minimizing the upper bound, we further optimize the scheduling of FLARE to exploit the channel heterogeneity. A nested problem structure is revealed to facilitate iteratively allocating the bandwidth with binary search and selecting devices with a new greedy method. A linear problem structure is also identified and a low-complexity linear programming scheduling policy is designed when training models have large Lipschitz constants. Experiments demonstrate that FLARE consistently outperforms the baselines in test accuracy, and converges much faster with the proposed scheduling policy. © 2002-2012 IEEE.

关键词： Resource allocation

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Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network for Prostate Cancer Diagnosis

Chaotic Particle Swarm Optimization Empowered Deep Convoluti...

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International Conference on Advances in Information technology (ICAIT)

作者： Gokulnath C N. Kalaiarasi Yeshaswini R P. Brinda Jeyakarthika K S.T. Gopukumar Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Kattankulathur Chengalpattu Department of ECE R.M.K college of engineering and technology Chennai Department of computer science Rajarajeshwari college of engineering Kumbalgodu Bangalore Department of Computer science and Engineering Veltech High tech Dr rangarajan Dr sakunthala engineering college Avadi Computer science and Engineering Ramco Institute of Technology Virudhunagar District Tamil Nadu Department of Research Activities and Biostatistics Sarada Krishna Homoeopathic Medical College Kanniyakumari District Tamilnadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Prostate cancer (PCa) Classification on magnetic resonance imaging (MRI) is an innovative use of medical imaging and machine learning (ML) models that objects to precisely recognize and classify PCa lesions in MRI scans. This advanced technique permits healthcare experts to analyze PCa more efficiently, define its level and severity, and improve modified treatment tactics. By analyzing subtle differences in tissue faces and recognizing cancerous areas, this methodology provides enhanced early recognition which is vital for patient results. It allows oncologists and radiologists to make knowledgeable verdicts, improving accuracy and efficacy of PCa diagnosis and successive treatment plans. This manuscript provides a proposal for Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network (CPSO-DCNN) technique for PCa Diagnosis. The developed CPSO-DCNN architecture incorporates a multi-faceted approach, starting with Wiener filtering (WF) for sound reduction, improving excellence and clarity of medical images. Feature extraction accomplished by employing SqueezeNet which is a great deep learning (DL) design expert at taking complex image patterns. This method further influences CPSO for hyperparameter tuning, enhancing its performance in order to support exact dataset characteristics. For critical task of PCa diagnosis, classification performed via bi-directional gated recurrent unit (BiGRU) networks and well-known for their capability to seize temporal needs within data. The simulation outcomes highlighted that CPSO-DCNN technique characterizes a substantial development in field of medical diagnostics. It provides a strong and exact solution for early recognition and classification of PCa.

关键词： Image recognition Magnetic resonance imaging Feature extraction Proposals Medical diagnosis Prostate cancer Particle swarm optimization Tuning Medical diagnostic imaging Principal component analysis

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Smart Signaling: A Smart Internet of Things Assisted Traffic Light Controlling and Monitoring System using Intelligent Sensors

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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International Conference on Science technology engineering and Management (ICONSTEM)

作者： G. Saranya R. Ratheesh S. Vijayalakshmi B. Arunsundar M. Swarna Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Agni College of Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE Dr. M.G.R. Educational and Research Institute University Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability.

关键词： System performance Urban areas Control systems Real-time systems Sensor systems Safety Internet of Things

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： A. Babisha G. Umarani Srikanth D.R. Angel Kiruba R Sundar Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing.

关键词： Sign language Visualization Translation Annotations Generative Pre-trainer transformer Transformers Feature extraction Multilingual Optimization Videos

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Federated Learning Approach for Tracking Malicious Activities in Cyber-Physical Systems

Federated Learning Approach for Tracking Malicious Activitie...

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2022 International Conference on Edge computing and Applications, ICECAA 2022

作者： Madala, Chandu Jagan Sekhar Yadav, G. Hemanth Kumar Sivakumar, S. Nithya, R. Manjunatha, K.M. Deivakani, M. Department of Computer Science & Engineering Lendi Institute of Engineering and Technology A ndhra Pradesh Vizianagaram535005 India Department of Computer Science Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur515701 India Department of Computer Science Engineering S.A. Engineering College Thiruverkadu Tamil Nadu 600077 India School of Computing Science KPR College of Arts Science and Research Tamil Nadu Coimbatore641048 India Department of Computer Science Government Polytechnic Karnataka Tumakuru572103 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul624622 India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

The fast rise of the Internet and advanced technologies causes an increase in network traffic, making network infrastructure increasingly complicated and varied. Mobile phones, wearable gadgets, and driverless cars are all instances of dispersed networks that create massive amounts of data every day. The processing capability of these devices has also increased steadily, necessitating the need to transport data, store data locally, and direct network calculations to edge devices. Intrusion detection systems are essential in guaranteeing the safety and confidentiality of such equipment. Deep Learning (DL) combined Intrusion Detection Systems (IDS) have gained prominence due to their excellent categorization accuracy. However, the requirement to store and communicate data to a centralized server may jeopardize privacy and security concerns. Federated learning (FL), on the other hand, fits in nicely as private information decentralized learning approach that does not transport data but instead trains algorithms locally and sends the parameters to a centralized server. This work targets to offer an extensive overview of the FL in intrusion detection systems. © 2022 IEEE.

关键词： Cyber Physical System

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A survey of Intel SGX and its applications

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Frontiers of Computer Science 2021年第3期15卷 191-205页

作者： Wei ZHENG Ying WU Xiaoxue WU Chen FENG Yulei SUI Xiapu LUO Yajin ZHOU School of Software and Microelectronics Northwestern Polytechnical UniversityXi'an 710129China Faculty of Engineering and Information Technology University of Technology SydneySydney 2007Australia Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

This paper presents a comprehensive survey on the development of Intel SGX(software guard extensions)processors and its *** the advent of SGX in 2013 and its subsequent development,the corresponding research works are also increasing *** order to get a more comprehensive literature review related to SGX,we have made a systematic analysis of the related papers in this *** first search through five large-scale paper retrieval libraries by keywords(i.e.,ACM Digital Library,IEEE/IET Electronic Library,SpringerLink,Web of Science,and Elsevier Science Direct).We read and analyze a total of 128 SGX-related *** first round of extensive study is conducted to classify *** second round of intensive study is carried out to complete a comprehensive analysis of the paper from various *** start with the working environment of SGX and make a conclusive summary of trusted execution environment(TEE).We then focus on the applications of *** also review and study multifarious attack methods to SGX framework and some recent security improvements made on ***,we summarize the advantages and disadvantages of SGX with some future research *** hope this review could help the existing and future research works on SGX and its application for both developers and users.

关键词： Intel SGX cloud computing trusted execution environment TrustZone AMD SEV

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Agriculture Monitoring System with Efficient Resource Management using IoT

Agriculture Monitoring System with Efficient Resource Manage...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： T. Tholhappiyan S. Sankar V. Selvakumar P. Robert Department of Information Technology Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Management Studies Sri Sairam Engineering College Chennai Tamil Nadu India Department of Management Studies Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India

IoT-based agriculture monitoring systems have recently been developed for effective resource management to maximize agricultural output. To collect real-time data on numerous factors, including soil moisture, temperature, humidity, light intensity, soil nutrient level, and animal health status, the proposed system uses wireless sensors placed in fields and livestock farms. Predictive analytics are generated for effective resource management utilizing cloud-based tools that process and evaluate the received data. The system also has a smartphone that lets farmers remotely monitor and manage different agricultural operations. Numerous agricultural procedures may be automated with the use of renewable energy can be captured and stored using energy harvesting and storage technologies to power the system. The system offers farmers real-time data, insights, and predictive analytics to support sustainable agriculture. This enables them to take smart actions and use available resources most.

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Evaluation and Implementation of Optimal Classification Algorithms for Credit Card Fraud Detection

Evaluation and Implementation of Optimal Classification Algo...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Sampath Kini K Pushpakumar. R R. Senthamil Selvan Praveenkumar G D Basi Reddy. A Department of Computer Science and Engineering Nitte Deemed To be University Karkala India Department of Information Technology Vel Tech Rangarajan Dr .Sangunthala R&D Institute of Science and Technology Avadi India Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Tirupati India Department of Computer Technology Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Nowadays, the credit card industry is more concerned about credit card fraud than any other problem. The main idea is to examine optional techniques used for fraud detection and to identify the different types of credit card fraud. Numerous strategies exist for lowering the prevalence of credit card fraud, each tailored to the specific threats faced by banks and the credit card industry. Credit card fraud prevention is the primary goal of implementing these measures. Existing methods have several unresolved issues that cause legitimate credit card customers to be mistakenly labelled as fraudulent. The amount of fraud in the financial sphere may be inferred from this paper's emphasis on integrating the best classification algorithm out of four distinct methods.

关键词： Industries Privacy Costs Prevention and mitigation Logic gates Credit cards Fraud Classification algorithms Data mining Detection algorithms

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