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Anonymity preserving lightweight authentication protocol for resource-limited wireless sensor networks

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High-Confidence Computing

High-Confidence Computing 2024年第2期4卷 82-95页

作者： vincent omollo nyangaresi Ganesh Keshaorao Yenurkar Jaramogi Oginga Odinga University of Science&Technology BondoKenya Yeshwantrao Chavan College of Engineering Wanadongri 441110India

Wireless sensor networks have been deployed in areas such as healthcare,military,transportation and home automation to collect data and forward it to remote users for further *** open wireless communication channels are utilized for data transmissions,the exchanged messages are vulnerable to various threats such as eavesdropping and message ***,many security solutions have been introduced to address these ***,the resource-constrained nature of the sensor nodes makes it inefficient to deploy the conventional security schemes which require long keys for improved ***,lightweight authentication protocols have been ***,majority of these schemes are still insecure while others incur relatively higher energy,computation,communication and storage *** this paper,a protocol that deploys only lightweight one-way hashing and exclusive OR operations is *** formal security analysis using Real-or Random(ROR)model demonstrates its capability to uphold the security of the derived session *** addition,its semantic security evaluation shows that it offers user privacy,anonymity,untraceability,authentication,session key agreement and key ***,it is shown to resist attacks such as side-channeling,physical capture,eavesdropping,offline guessing,spoofing,password loss,session key disclosure,forgery and *** terms of performance,it has relatively lower communication overheads and improves the computation costs and supported security characteristics by 31.56%and 33.33%respectively.

关键词： Anonymity Authentication Formal security Lightweight Privacy Sensor WSN

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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Computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James vincent omollo nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： vincent omollo nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

Efficient Authentication Algorithm for Secure Remote Access in Wireless Sensor Networks

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Journal of Computer Science Research 2021年第4期3卷 43-50页

作者： Peter Sungu Nyakomitta vincent omollo nyangaresi Solomon Odhiambo Ogara Faculty of Biological&Physical Sciences Tom Mboya University CollegeHomabayKenya School of Informatics and Innovation Systems Jaramogi Oginga Odinga University of Science and TechnologyBondoKenya

Wireless sensor networks convey mission critical data that calls for adequate privacy and security *** accomplish this objective,numerous intrusion detection schemes based on machine learning approaches have been *** addition,authentication and key agreements techniques have been developed using techniques such as elliptic curve cryptography,bilinear pairing operations,biometrics,fuzzy verifier and Rabin ***,these schemes have either high false positive rates,high communication,computation,storage or energy requirements,all of which are not ideal for battery powered sensor ***,majority of these algorithms still have some security and privacy challenges that render them susceptible to various *** this paper,a WSN authentication algorithm is presented that is shown to be robust against legacy WSN privacy and security attacks such as side-channel,traceability,offline guessing,replay and *** a performance perspective,the proposed algorithm requires the least computation overheads and average computation costs among its peers.

关键词： Attacks Authentication ECC Key agreement Privacy Security WSN

Efficient Feature Selection and ML Algorithm for Accurate Diagnostics

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Journal of Computer Science Research 2022年第1期4卷 10-19页

作者： vincent omollo nyangaresi Nidhal Kamel Taha El-Omari Judith Nyakanga Nyakina Faculty of Biological&Physical Sciences Tom Mboya University CollegeHomabayKenya The World Islamic Science and Education University AmmanJordan School of Nursing Uzima UniversityKisumuKenya

Machine learning algorithms have been deployed in numerous optimization,prediction and classification *** has endeared them for application in fields such as computer networks and medical *** these machine learning algorithms achieve convincing results in these fields,they face numerous challenges when deployed on imbalanced ***,these algorithms are often biased towards majority class,hence unable to generalize the learning *** addition,they are unable to effectively deal with high-dimensional ***,the utilization of conventional feature selection techniques from a dataset based on attribute significance render them ineffective for majority of the diagnosis *** this paper,feature selection is executed using the more effective Neighbour Components Analysis(NCA).During the classification process,an ensemble classifier comprising of K-Nearest Neighbours(KNN),Naive Bayes(NB),Decision Tree(DT)and Support Vector Machine(SVM)is built,trained and ***,cross validation is carried out to evaluate the developed ensemble *** results shows that the proposed classifier has the best performance in terms of precision,recall,F-measure and classification accuracy.

关键词： Accuracy Classifier Ensemble F-measure Machine learning Precision Recall