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Feedback-based trust module for IoT networks using machine learning

International Journal of Wireless and Mobile Computing 2024年第1期27卷 78-91页

作者： Iqbal, Sania Qureshi, Shaima Department of Computer Science and Engineering National Institute of Technology Jammu & Kashmir Srinagar India

The increasing emergence of IoT in healthcare, industrial automation, manufacturing, infrastructure, business and the home undoubtedly provides more conveniences in different aspects of human life. Any IoT security and trust solution must consider the heterogeneity and mobility of IoT devices, and the features of other devices connected to the network. Traditional security measures face computation, storage and communication challenges in securing IoT, and the increasing reliance on IoT services and applications has exacerbated this issue. It is crucial to identify a security management model to handle these constraints. Trust as a security regime in the IoT has far-reaching implications. This paper proposes a novel trust-based security mechanism by combining machine learning and traditional summations to determine a node’s trustworthiness in an IoT environment in milliseconds. Based on this, a computational trust management model is designed by aggregating direct and feedback-based trust information. The model parameters like the trust breach window and trust re-initiation point are selected based on extensive experimentation to dynamically calculate each node’s trust value to reach the proposed model’s peak performance. The model’s effectiveness is verified through simulation. In 500 iterations, the proposed model achieved 97% accuracy with a run time of 0.37 seconds. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Network security

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Osmotic Computing Based Secure Resource Orchestration Scheme for Vehicular Communication 17

Osmotic Computing Based Secure Resource Orchestration Scheme...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Garg, Deepanshu Angurala, Mohit Bali, Rasmeet S. Kumar, Neeraj Chandigarh University Computer Science & Engineering Department India Guru Nanak Dev University Computer Science Department India Thapar University Patiala Computer Science & Engineering Department India

ISBN: (纸本)9798331531195

The advancement in technology leads to provide an efficient communication among vehicles to offload resource-intensive tasks for transportation-based services. However, it may cause issue related to efficient secure resource utilization among vehicular nodes. The key reason behind these considerations are the high mobility of vehicles and their high computational requirements for task execution. To solve these issues, an osmotic computing based vehicular communication scheme has been proposed. The primary objective of the scheme is to provide a communication model which can perform effective secure resource management among static or dynamic vehicular nodes through Osmotic Computing. By utilizing the key components of osmotic computing such as osmotic resource manager and node manager, numerous resources have been shared with the vehicles in an effective way. The performance of the proposed scheme has been evaluated using simulation. Results obtained show that the proposed scheme outperform the existing schemes in terms of various performance evaluation parameters. © 2025 IEEE.

关键词： Data privacy

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Traffic engineering and Quality of Service in Hybrid Software Defined Networks

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China Communications 2024年第2期21卷 96-121页

作者： Samiullah Mehraban Rajesh Kumar Yadav IOT Lab Department of Computer Science&EngineeringDelhi Technological UniversityDelhi 110042India

For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.

关键词： DRL hSDN QoE QoS SDN TE

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Fraud Detection in Banking using the Kaggle Credit Card Dataset and XGBoost Model 5

Fraud Detection in Banking using the Kaggle Credit Card Data...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ango, Rithika Masih, Raj Kumar Reddy, C Kishor Kumar Shuaib, Mohammed Monika Singh, T. Alam, Shadab Stanley College of Engineering & Technology for Women Department of Computer Science and Engineering Hyderabad India Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798331518097

The threat posed by credit card fraud, and by extension, online banking, continues to grow with the convenience brought forth by online banking services. Many financial institutions and customers stand at great risk because of this reality. The purpose of the paper is to enhance the security of banking systems through advanced technologies - machine learning. The research employed the well-known dataset from Kaggle Credit Card, which contains numerous records of transactions labeled as either fraud or not, thus presenting an aspect similar to real life imbalanced data. The main focus in the present work is the XGBoost model which involves one of the ensemble learning methods, which is characterized by a high level of precision and fast execution times. As a result of hyperparameter optimization and feature engineering, the paper also works on the performance of the algorithm improving its ability to detect fraudulent transactions in a sea of normal transactions. The results obtained from the experiments show that the XGBoost model is not only proven to be more accurate than earlier approaches to fraud detection, but also greatly reduces the number of false positives. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. The metrics in which the model suggested outperformed the available approaches are, the accuracy is 98.60%, error rate is 2.10% while its precision reached 95.40, recall of 92.30, F1 score 93.20 and AUC-ROC 99.70. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. © 2024 IEEE.

关键词： Banking

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Resource scheduling in cloud environment using particle swarm search algorithm

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International Journal of Cloud Computing 2024年第4期13卷 330-352页

作者： Majhi, Malay Kumar Kabat, Manas Ranjan Sahoo, Satya Prakash Department of Computer Science and Engineering Veer Surendra Sai University of Technology Sambalpur India

Cloud computing has gained significant popularity as a platform for processing large-scale data analytics, offering benefits such as high availability, robustness, and cost-effectiveness. However, job scheduling in cloud systems presents a major challenge, as it directly impacts execution time and operational costs. To address these issues, this paper presents a novel multi-adaptive convergent particle swarm optimisation (MAC-PSO) algorithm designed to decrease the failure rate, minimise makespan values, and enhance resource utilisation. The round Robin scheduling method aids in task execution by determining the appropriate time-space allocation. The proposed algorithm’s performance is compared to that of the TLBO algorithm, demonstrating that MAC-PSO outperforms both TLBO and the original PSO. Moreover, a comprehensive analysis is proposed to evaluate the performance metrics within the MAC-PSO algorithm. Notably, MAC-PSO effectively increases the ratio of solutions that dominate previous algorithmic approaches and identifies a greater number of solutions that cater to user preferences. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud platforms

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Constructing a Secure Charity NFT Auction Platform Using Fisco Bcos Blockchain for Enhancing Transparency and Traceability

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IEEE Access 2024年 12卷 36924-36941页

作者： Chen, Chin-Ling Zhan, Wan-Bing Tsaur, Woei-Jiunn Huang, Der-Chen Liu, Ling-Chun Changchun Sci-Tech University Changchun School of Information Engineering Jilin130600 China Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung413310 Taiwan Xiamen University of Technology Xiamen School of Computer and Information Engineering Fujian361024 China National Taipei University Computer Center New Taipei City237303 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung402202 Taiwan

In the charity sector, fundraising and transparency have long been key issues. Charity NFT (Non-Fungible Token) auctions, an emerging charity fundraising model integrating blockchain and NFT concepts, bring opportunities and challenges. Blockchain provides distributed data integrity and transparency via cryptography-linked data blocks, while NFTs enable unique digital ownership representation. This study designs a charity NFT auction platform on the Fisco Bcos blockchain, using multi-signature algorithms to ensure NFT authenticity, ECDSA (Elliptic Curve Digital Signature Algorithm) signatures for transaction integrity and traceability, NFTs and virtual currencies to reduce costs, and IPFS (InterPlanetary File System) for storage. The implemented system achieves 2104 TPS throughput with 492-millisecond latency, increasing transaction processing with low latency. Overall, the platform aims to address charity issues like opaque fund flows, high costs, and fake initiatives through the strategic application of blockchain and NFT functionality. © 2013 IEEE.

关键词： Smart contract

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Architectural framework for multiplayer cooperative cloud gaming to optimise quality of service

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International Journal of Networking and Virtual Organisations 2024年第2期30卷 174-205页

作者： Srilakshmi, Nimmagadda Kannan, Naresh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The gaming industry is getting more attraction from cloud services providing gaming applications for cooperative multiplayer gaming. Real-time services like cloud gaming are possible by performing necessary process-intensive tasks within the cloud. In this paper, an architectural model for supporting cooperative gaming towards multiplayer is proposed to improve the quality of service in terms of bandwidth and latency compared to existing architectural models. According to this study, the time it takes to change a video has grown by 95% compared to how well ad hoc mobile cloudlets and the cloud do at sharing videos. Although P-frames, I-frames, and B-frames account for 23%, 78%, and 94% of the resource consumption, density in intra-stream P-frames is also considered. Similarly, resource and CPU use based on skewness are effective compared to mobile devices' 95% faster video sharing and 77% shorter link delays. © 2024 Inderscience Enterprises Ltd.

关键词： Quality of service

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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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Image Guidance Encoder-Decoder Model in Image Captioning and Its Application

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第9期51卷 1385-1392页

作者： Yang, Zhen Zhou, Ziwei Wang, Chaoyang Xu, Liang School of Applied Technology University of Science and Technology Liaoning Anshan China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan China

This paper introduces a new network model - the Image Guidance Encoder-Decoder Model (IG-ED), designed to enhance the efficiency of image captioning and improve predictive accuracy. IG-ED, a fusion of the convolutional network VGGNet-16 and the long short-term memory network (LSTM), is designed based on the encoder-decoder structure. The image captioning performance sees significant enhancements when leveraging the IG-ED network model. The network training process unfolds in a series of steps. Initially, the input image undergoes convolution via the VGGNet-16 network, producing a 512-dimensional vector. Concurrently, each word in the image's caption is encoded to generate a corresponding 512-dimensional vector consistent with the image feature dimension. These two vectors form the input for the decoding process. Subsequently, the vectors are fed into the redesigned fusion LSTM (F-LSTM) network at different time steps to gradually train the parameters of the IG-ED framework. The training process is completed by utilizing a loss function for determining convergence. Evaluation of the IG-ED model's performance is conducted using CIDEr and seven other evaluation metrics on the MSCOCO 2014 dataset. The results exhibit substantial improvements over the "Adaptive Attention Mode" network and "Neural Talk" network. Additionally, the parameter count of the IG-ED architecture is significantly reduced compared to the "Adaptive Attention Mode" network, leading to decreased computational resource requirements and enabling edge computing on the neural network. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

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Explainable Anomaly Detection Using Vision Transformer Based SVDD

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computers, Materials & Continua 2023年第3期74卷 6573-6586页

作者： Ji-Won Baek Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si 16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si 16227Korea

Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic *** is possible to offer the explainable basis of decision-making for inference *** the causality of risk factors that have an ambiguous association in big medical data,it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease *** addition,the technique makes it possible to accurately predict disease risk for anomaly *** transformer for anomaly detection from image data makes classification through ***,in MLP,a vector value depends on patch sequence information,and thus a weight *** should solve the problem that there is a difference in the result value according to the change in the *** addition,since the deep learning model is a black box model,there is a problem that it is difficult to interpret the results determined by the ***,there is a need for an explainablemethod for the part where the disease *** solve the problem,this study proposes explainable anomaly detection using vision transformerbasedDeep Support Vector Data Description(SVDD).The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision *** order to draw the explainability of model results,it visualizes normal parts through *** health data,both medical staff and patients are able to identify abnormal parts *** addition,it is possible to improve the reliability of models and medical *** performance evaluation normal/abnormal classification accuracy and f-measure are evaluated,according to whether to apply *** Results The results of classification by applying the proposed SVDD are evaluated ***,through the proposed meth

关键词： Explainable AI anomaly detection vision transformer SVDD health care deep learning classification

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