Transactions on Emerging Telecommunications Technologies最新文献_第6页

SDHO-KGNN: An Effective Knowledge-Enhanced Optimal Graph Neural Network Approach for Fraudulent Call Detection SDHO-KGNN：一种有效的知识增强最优图神经网络欺诈呼叫检测方法

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-20 DOI: 10.1002/ett.70101

Pooja Mithoo, Manoj Kumar

{"title":"SDHO-KGNN: An Effective Knowledge-Enhanced Optimal Graph Neural Network Approach for Fraudulent Call Detection","authors":"Pooja Mithoo, Manoj Kumar","doi":"10.1002/ett.70101","DOIUrl":"https://doi.org/10.1002/ett.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>Rapid advancements in mobile communication technologies have led to the progression of telecom scams that not only deplete individual fortunes but also affect social income. Hence, fraudulent call detection gains significance, which not only aims to proactively recognize the frauds, but also alleviate the fraudulent activities to manage external losses. Though the traditional methods, such as rule-based systems and supervised machine learning techniques, actively engage in detecting such fraudulent activities, they fail to adapt to the evolving fraud patterns. Therefore, this research introduces a sheepdog hunt optimization-enabled knowledge-enhanced optimal graph neural network classifier (SDHO-KGNN) approach for detecting fraudulent calls accurately. The effectiveness of the proposed SDHO-KGNN approach is achieved through the combination of the power of graph representation learning with expert insights, which allows the proposed SDHO-KGNN approach to capture complex relationships and patterns within telecom data. Additionally, the integration of the SDHO algorithm enhances model performance by optimizing the discrimination between legitimate and fraudulent calls. Moreover, the SDHO-KGNN classifier captures the intricate call patterns and relationships within dynamic call networks, thereby attaining a better accuracy, precision, and recall of 93.8%, 95.91%, and 95.53% for 90% of the training.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Wireless System Secrecy Capacity Through NOMA Scheme and Multiple UAV-Mounted IRSs Amidst Colluding and Non-Colluding Eavesdroppers 在串通窃听者和非串通窃听者中，通过NOMA方案和多个无人机机载IRSs增强无线系统的保密能力

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-19 DOI: 10.1002/ett.70135

Duc Thinh Vu, Ba Cao Nguyen, Tran Manh Hoang, Taejoon Kim, Bui Vu Minh, Phuong T. Tran

{"title":"Enhancing Wireless System Secrecy Capacity Through NOMA Scheme and Multiple UAV-Mounted IRSs Amidst Colluding and Non-Colluding Eavesdroppers","authors":"Duc Thinh Vu, Ba Cao Nguyen, Tran Manh Hoang, Taejoon Kim, Bui Vu Minh, Phuong T. Tran","doi":"10.1002/ett.70135","DOIUrl":"https://doi.org/10.1002/ett.70135","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a novel approach to enhance the secrecy performance of nonorthogonal multiple access (NOMA) systems by leveraging multiple unmanned aerial vehicles (UAVs) equipped with intelligent reflecting surfaces (IRSs). In this proposed system, multiple UAVs mounted with IRSs (shortened as U/Ss) are strategically deployed to assist two legitimate users in the presence of multiple colluding eavesdroppers (CEs) or noncolluding eavesdroppers (NCEs) attempting to intercept messages. The paths from the transmitter to the users are combined with those involving the U/Ss to maximize the received message power. We derive mathematical expressions for the secrecy capacities (SCs) of the proposed U/S-NOMA systems over Nakagami-<span></span><math></math> channels. Additionally, asymptotic expressions for SCs in the high transmit power region are provided. Numerical results demonstrate that the SCs of U/S-NOMA systems significantly surpass those of traditional NOMA networks lacking U/Ss. Notably, the U/S-NOMA systems achieve their highest SCs more rapidly than traditional NOMA systems. Consequently, the integration of U/Ss proves effective in reducing transmit power and enhancing the secrecy performance of NOMA systems. Furthermore, we also delve into the impact of key parameters such as the number of reflecting elements (REs) in U/Ss, carrier frequency, U/Ss' positions, fading order, bandwidth, number of eavesdroppers, and NOMA power allocation coefficients. Valuable recommendations are presented based on a thorough investigation of these crucial parameters.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Autonomous Vehicle Security: Federated Learning for Detecting GPS Spoofing Attack 增强自动驾驶汽车的安全性：联合学习检测 GPS 欺骗攻击

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-16 DOI: 10.1002/ett.70138

Maqsood Muhammad Khan, Mohsin Kamal, Maliha Shabbir, Saad Alahmari

{"title":"Enhancing Autonomous Vehicle Security: Federated Learning for Detecting GPS Spoofing Attack","authors":"Maqsood Muhammad Khan, Mohsin Kamal, Maliha Shabbir, Saad Alahmari","doi":"10.1002/ett.70138","DOIUrl":"https://doi.org/10.1002/ett.70138","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous vehicles (AVs) are poised to transform modern transportation, providing superior traffic management and improved user experiences. However, there exists a considerable risk to the acquisition of Position, Velocity and Time (PVT) in AVs, since the acquisition of PVT is vulnerable to Global Positioning System (GPS) spoofing attacks that could redirect the AV to wrong paths or lead to security threats. To address these issues, we propose a novel approach for detecting GPS spoofing attacks in AVs using Federated Learning (FL) with trajectories obtained from the Car Learning to Act (CARLA) simulator. Each vehicle autonomously performs localization using sensor data that includes yaw rate, steering angle, as well as wheel speed. The obtained localized coordinates (authentic and spoofed) are utilized to compute weights. These weights are aggregated at the Roadside Unit (RSU) and shared with the global model utilizing Support Vector Machines (SVM) for classification. The global model updates local models through FL, ensuring data privacy and collaborative learning. The experimental results show that the proposed model achieves 99% accuracy, 98% F1 score, and the AUC-ROC of 99% outperforming traditional machine learning methods including the K-Nearest Neighbors (KNN) and Random Forest (RF). The results demonstrate the practicality of using FL to improve the security of AVs against GPS spoofing attacks with limited data sharing, thereby offering a potential approach for real-world applications.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm 基于Gazelle优化算法的贝叶斯加权随机森林多类DDOS攻击检测分类

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-15 DOI: 10.1002/ett.70092

R. Barona, E. Babu Raj

{"title":"Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm","authors":"R. Barona, E. Babu Raj","doi":"10.1002/ett.70092","DOIUrl":"https://doi.org/10.1002/ett.70092","url":null,"abstract":"<div>\u0000 \u0000 <p>The increase in Distributed Denial of Service (DDoS) attacks poses a considerable threat to the security and stability of the current network, especially in Internet of Things (IoT) and cloud environments. Traditional detection methods often struggle with the inability to achieve a balance between detection accuracy and computational efficiency. In this manuscript, the Classification of Multiclass DDOS Attack Detection using Bayesian Weighted Random Forest Optimized with Gazelle Optimization Algorithm (DDOS-AD-BWRF-GOA) is proposed. First, the raw data is gathered from the CICDDoS2019 dataset. Then, input data are preprocessed utilizing Adaptive Bitonic Filtering for normalizing the values. The preprocessed data are fed to the Improved Feed Forward Long Short-Term Memory technique for selecting features that increase the model's execution time. The selected features are supplied to the Bayesian Weighted Random Forest (BWRF), which classifies the multiclass DDOS attack. In general, Bayesian Weighted Random Forest does not adopt any optimization methods to define optimal parameters to guarantee exact DDOS identification. Hence, GOA is proposed to optimize the Bayesian Weighted Random Forest classifier. The proposed method is implemented in MATLAB. The performance metrics, such as Accuracy, Precision, Recall, <i>F</i>1-score, Specificity, Error rate, and Computational time are evaluated. The proposed method attains 15.34%, 24.1%, and 18.9% higher accuracy and 12.4%, 18.24%, and 22.6% higher precision when analyzed with existing techniques: Hybrid deep learning method for DDOS detection and classification (HDL-DDOS-DC), Edge-HetIoT Defense against DDoS attack utilizing learning techniques (EHD-DDOS-LT), and Digital twin-enabled intelligent DDOS detection for autonomous core networks (DTI-DDOS-ACN), respectively.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Resource Provisioning in Cloud Computing Using Optimized Wasserstein Deep Convolutional Generative Adversarial Networks 基于优化Wasserstein深度卷积生成对抗网络的云计算动态资源配置

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-15 DOI: 10.1002/ett.70128

C. Santhiya, S. Padmavathi

{"title":"Dynamic Resource Provisioning in Cloud Computing Using Optimized Wasserstein Deep Convolutional Generative Adversarial Networks","authors":"C. Santhiya, S. Padmavathi","doi":"10.1002/ett.70128","DOIUrl":"https://doi.org/10.1002/ett.70128","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud computing (CC) has revolutionized the way resources are managed and delivered by providing scalable, on-demand services. However, dynamic resource provisioning remains a complex challenge due to unpredictable workloads, varying user demands, and the need to maintain cost efficiency. Traditional resource allocation techniques lack the adaptability required to optimize resource usage under dynamic conditions. This manuscript presents a novel approach for dynamic resource provisioning using an Optimized Wasserstein Deep Convolutional Generative Adversarial Network (DRP-WDCGAN-AHBA). Initially, the input data are collected from the Grid Workloads Dataset, which provides a comprehensive representation of workload patterns in cloud environments. The input data undergo rigorous preprocessing using Adaptive Self-Guided Filtering (ASGF) to ensure data quality. Then, Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN) is used to forecast CPU utilization over specified time intervals of 5, 15, 30, and 60 min. The Adaptive Hybrid Bat Algorithm (AHBA) is employed to optimize resource allocation dynamically and ensure efficient utilization. The proposed DRP-WDCGAN-AHBA model attains 20.36%, 18.63%, and 21.24% lower energy consumption and 16.78%, 23.64%, and 26.32% lower response time when compared with existing models, such as Multi-agent QoS-aware autonomic resource provisioning method BPM in containerized multi-cloud environs for elastic (DRP-QoS-EDSAE), Multi-objective dependent Scheduling Method for Effective Resource Utilization in Cloud Computing (DRP-LS-CSO-ARNN), and Energy-aware fully adaptive resource provisioning in collaborative CPU-FPGA cloud environs: Journal of Parallel and Distributed Computing (EFARP-CPU-FPGA).</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain Assisted Secure Authentication Protocol for Aerial Surveillance in IoT-Based Smart Agriculture 基于物联网的智能农业中空中监控的区块链辅助安全认证协议

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-13 DOI: 10.1002/ett.70118

G. S. Tamizh Arasi, P. Rubini, K. C. Sriharipriya, Achyut Shankar, Bharat Bhushan, Abhay Bansal

{"title":"Blockchain Assisted Secure Authentication Protocol for Aerial Surveillance in IoT-Based Smart Agriculture","authors":"G. S. Tamizh Arasi, P. Rubini, K. C. Sriharipriya, Achyut Shankar, Bharat Bhushan, Abhay Bansal","doi":"10.1002/ett.70118","DOIUrl":"https://doi.org/10.1002/ett.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>In modern smart agriculture, unmanned aerial vehicles (UAVs) play a crucial role in data acquisition, crop monitoring, and precision farming using high-resolution cameras and advanced sensors. However, the extensive use of IoT devices and aerial surveillance systems introduces significant security challenges, making drone-captured data vulnerable to unauthorized access, cyberattacks, and data tampering. As multiple entities collaborate in smart agriculture, the need for a secure and efficient authentication mechanism becomes critical. To address these concerns, this paper presents a blockchain-assisted secure two-factor mutual authentication scheme for aerial surveillance security in IoT-enabled smart agriculture. The key contributions are twofold: (1) A blockchain-based authentication framework that ensures decentralized, tamper-proof security, and (2) an efficient and lightweight authentication mechanism using physically unclonable functions (PUF) to enhance device authentication and mitigate impersonation threats. The proposed protocol is formally verified using automated validation of internet security protocols and applications (AVISPA) to assess its security robustness. The results demonstrate that the protocol effectively defends against major security threats while maintaining low computational complexity (1.11 ms). Comparative analysis indicates that the proposed approach outperforms conventional authentication schemes, offering enhanced security, scalability, and efficiency in IoT-based aerial surveillance security for smart agriculture.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Fire Hawk With Successive Convex Approximation for Sum Rate Maximization Problem in Double IRS-Assisted Multi-User MIMO mmWave Systems 基于连续凸逼近的混合火鹰法求解双irs辅助多用户MIMO毫米波系统和速率最大化问题

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-13 DOI: 10.1002/ett.70125

Ragodaya Deepthi Kadiyala, Anjaneyulu Lokam, Chayan Bhar

{"title":"Hybrid Fire Hawk With Successive Convex Approximation for Sum Rate Maximization Problem in Double IRS-Assisted Multi-User MIMO mmWave Systems","authors":"Ragodaya Deepthi Kadiyala, Anjaneyulu Lokam, Chayan Bhar","doi":"10.1002/ett.70125","DOIUrl":"https://doi.org/10.1002/ett.70125","url":null,"abstract":"<div>\u0000 <p>A promising technique for improving spectral efficiency in wireless communication is Intelligent Reflecting Surfaces (IRS). However, because of significant path loss and obstructions, a single IRS is not enough to provide adequate coverage and beamforming gain in millimeter-wave (mmWave) networks. To overcome these limitations, this paper investigates the impact of a dual-IRS-assisted multi-user MIMO mmWave system, which enables cooperative passive beamforming to enhance the effective channel gain and extend coverage in non-line-of-sight (NLoS) environments. The proposed approach optimizes phase shift design at the IRSs and digital precoding at the transmitter by formulating a weighted sum rate maximization issue. To effectively solve the precoding and phase shift design problem, a hybrid metaheuristic optimization framework that combines Bernstein-Levy Search Differential Evolution (BL-SDE), Hybrid Aquila with Fire Hawk (HAOFH) optimization, and Double Stochastic Successive Convex Approximation (DSSCA) is generated. The hybrid Aquila optimizer specifically solves the digital precoding matrix design challenge, while the Fire Hawk optimizer solves the analog phase shift problem. Throughput maximization is a critical indicator for assessing IRS-assisted mmWave MIMO systems, and its direct impact on network efficiency and user experience is the driving force for its adoption as the performance metric. According to simulation results, the suggested dual-IRS system outperforms traditional single-IRS and non-IRS-assisted schemes in terms of spectral efficiency, sum rate, bit error rate, and mean square error. These findings support the efficiency of the dual-IRS framework in addressing mmWave channel defects and promoting next-generation wireless communication.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Analysis of Ethereum Blockchain Enabled IoT Based Model for Secure Data Transmission 基于以太坊区块链的物联网安全数据传输模型设计与分析

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-10 DOI: 10.1002/ett.70126

Sapna S. Khapre, Santosh Kumar Sahoo

{"title":"Design and Analysis of Ethereum Blockchain Enabled IoT Based Model for Secure Data Transmission","authors":"Sapna S. Khapre, Santosh Kumar Sahoo","doi":"10.1002/ett.70126","DOIUrl":"https://doi.org/10.1002/ett.70126","url":null,"abstract":"<div>\u0000 \u0000 <p>Ensuring the security and privacy of sensitive health data in Internet of Things (IoT)-based healthcare systems (HCS) is a critical challenge. This paper proposes a robust security framework by integrating blockchain mechanisms and deep learning (DL) approaches to enhance security and data privacy. The proposed framework leverages the Ethereum blockchain with zero knowledge proof (ZKP) to ensure data integrity and confidentiality, while the interplanetary file system (IPFS) provides secure and efficient data storage. Additionally, a novel At-GAN-BiLSTM model is introduced for intrusion detection by combining the attention mechanism, generative adversarial networks (GAN) and bidirectional long short-term memory (Bi-LSTM) to improve detection accuracy and also help to enhance model robustness. The proposed model is evaluated by two different benchmark datasets, namely CICIDS-2018 (D1) and ToN-IoT (D2), achieving accuracies of 99.9% and 99.1%, respectively. Comparative investigation shows that the proposed approach reduces false alarm rates (FAR) and performs better than current models in identifying impersonation, insider, and man-in-the-middle (MITM) attacks. By integrating blockchain and DL, the proposed framework significantly enhances intrusion detection, data security, and overall system resilience, addressing key vulnerabilities in IoT-based healthcare security.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to “Lattice Homomorphic Assisted Privacy Preserving Electronic Health Records Data Transmission in Internet of Medical Things Using Blockchain” 对“基于b区块链的医疗物联网中格同态辅助隐私保护电子病历数据传输”的修正

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-07 DOI: 10.1002/ett.70124

引用次数: 0

Secure Transactions in IoT Network Using PINN-Based Intrusion Detection System and HDPoA Blockchain Protocol 基于pup入侵检测系统和HDPoA区块链协议的物联网安全交易

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-07 DOI: 10.1002/ett.70116

Sourav Mishra, Vijay Kumar Chaurasiya

{"title":"Secure Transactions in IoT Network Using PINN-Based Intrusion Detection System and HDPoA Blockchain Protocol","authors":"Sourav Mishra, Vijay Kumar Chaurasiya","doi":"10.1002/ett.70116","DOIUrl":"https://doi.org/10.1002/ett.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>IoT is a rapidly developing technology with a wealth of creative application possibilities. However, IoT wireless sensor networks are vulnerable to Denial of Service (DoS) attacks due to their insecure nature. Although network integrity and security have been ensured through the use of distributed ledger and blockchain technologies, privacy preservation concerns frequently arise with traditional approaches. So, deep learning-based Physics-informed neural networks (PINN) and Honesty-based Distributed Proof-of-Authority (HDPoA) are developed to enhance transaction security and detect intrusions. Initially, the mobile nodes were deployed in different regions to gather transactions and an intrusion detection system to analyze attacks. First, the Intrusion Detection System (IDS) uses a deep learning approach for detecting the intrusion in the network. For that, the collected data from the deployed nodes are pre-processed using Variational auto-encoder and min-max normalization to standardize input dataset values. Then the features are selected using wild horse optimization and classified using PINN to predict data attack or non-attack. After that, Homomorphic variable tag generation is used for normal transactions with multiple copies in the same document, which are then converted into hash values using the Keccak hashing function. The miner validates transactions based on rank-based priority. Honesty-based Distributed Proof-of-Authority (HDPoA) was used for network security, making it suitable for deployment in blockchain-based IoT applications. The proposed deep learning-based PINN classifier reached 97.2% accuracy and 96.52% specificity. Homomorphic variable (HV) tag generation takes 0.4 s, while the Keccak algorithm takes 0.3 s for hash generation, and the HDPoA protocol has 420 s for block generation time.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0