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Cross-Layer Hidden Markov Analysis for Intrusion Detection

K. Venkatachalam1, P. Prabu2, B. Saravana Balaji3, Byeong-Gwon Kang4, Yunyoung Nam4,*, Mohamed Abouhawwash5,6

1 Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India
2 Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India
3 Department of Information Technology, Lebanese French University, Erbil, 44001, KR, Iraq
4 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea
5 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
6 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, USA

* Corresponding Author: Yunyoung Nam. Email: email

Computers, Materials & Continua 2022, 70(2), 3685-3700. https://doi.org/10.32604/cmc.2022.019502

Abstract

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.

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APA Style
Venkatachalam, K., Prabu, P., Balaji, B.S., Kang, B., Nam, Y. et al. (2022). Cross-layer hidden markov analysis for intrusion detection. Computers, Materials & Continua, 70(2), 3685-3700. https://doi.org/10.32604/cmc.2022.019502
Vancouver Style
Venkatachalam K, Prabu P, Balaji BS, Kang B, Nam Y, Abouhawwash M. Cross-layer hidden markov analysis for intrusion detection. Comput Mater Contin. 2022;70(2):3685-3700 https://doi.org/10.32604/cmc.2022.019502
IEEE Style
K. Venkatachalam, P. Prabu, B.S. Balaji, B. Kang, Y. Nam, and M. Abouhawwash "Cross-Layer Hidden Markov Analysis for Intrusion Detection," Comput. Mater. Contin., vol. 70, no. 2, pp. 3685-3700. 2022. https://doi.org/10.32604/cmc.2022.019502



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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