Cross-layer hidden Markov analysis for intrusion detection
Data transition; End-to-end delay; Energy consumption; Flexibility; Hidden Markov model; Intrusion detection; Link optimization; Packet delivery ratio; Privacy; Searching; Security; Throughput
Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion detection methods. First, the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks. Moreover, delay, energy consumption, network lifetime, and link accomplishment are highly addressed by the proposed algorithm. The hidden Markov model is used to maintain the data transition and distributions in the network. Every data communication network, like ad hoc mobile cloud computing, faces security and confidentiality issues. However, the main security issues in this article are addressed using the storage and search optimization approach. Hence, the new algorithm developed helps detect intruders through intelligent cross layer analysis with the Markov model. The proposed model was simulated in Network Simulator 3, and the outcomes were compared with those of prevailing methods for evaluating parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput. 2022 Tech Science Press. All rights reserved.
Venkatachalam K.; Prabu P.; Balaji B.S.; Kang B.-G.; Nam Y.; Abouhawwash M.
Computers, Materials and Continua, Vol-70, No. 2, pp. 3685-3700.
https://doi.org/10.32604/cmc.2022.019502 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116038499&doi=10.32604%2fcmc.2022.019502&partnerID=40&md5=4de96ae63afd97d56ddb65bcf5348c65
Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Balaji B.S., Department of Information Technology, Lebanese French University, KR, Erbil, 44001, Iraq; Kang B.-G., Department of ICT Convergence, Soonchunhyang University, Asan, 31538, South Korea; Nam Y., Department of ICT Convergence, Soonchunhyang University, Asan, 31538, South Korea; Abouhawwash M., Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt, Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, United States