International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 986KB)
Adaptive Trust Node Routing via Energy-Aware and Steerable Network with Stochastic Optimization for Augmented Intrusion Detection in MANET Infrastructures
PDF (986KB), PP.221-239
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Author(s)
Mehaboob Mujawar
1,*
R. Mohandas
2
R. Giri Prasad
3
K. Narasimha Raju
4
Index Terms
Trust Node Evaluation, Intrusion Detection, MANET, Energy-Driven Collaborative Routing, Manifold-Aware Point-wise Steerable Network, Stochastic Emperor Penguin Optimization
Abstract
Mobile Ad-Hoc Networks (MANETs) are self-organizing networks without any fixed infrastructure, which are decentralized and, thus, find applications in a dynamic and infrastructure-less environment. Nevertheless, these networks face significant challenges, including excessive energy consumption, malfunctioning routing, and security risks posed by malicious nodes. Such issues tend to cause higher communication overhead and a shorter network lifetime, particularly in highly mobile environments. To overcome these challenges, this paper proposes a dynamic, energy-saving routing architecture that could improve the network's Security and reliability. The suggested solution assesses the reliability of network nodes through energy performance and communication reliability, and continuously analyzes network traffic to identify malicious activity in real time. Data delivery is guaranteed through secure route selection and smart intrusion detection, allowing the framework to reduce superfluous energy consumption. The obtained simulation outcomes show that the suggested approach achieves 92.8% and 96.5% in the packet delivery ratio and intrusion detection, respectively, which is a strong indication of an impervious defense against attacks. Moreover, the method is highly energy-efficient, has a longer network lifetime, and is thus a good fit for the practice of MANET use, such as emergency response, military communications, and mobile IoT networks.
Cite This Paper
Mehaboob Mujawar, R. Mohandas, R. Giri Prasad, K. Narasimha Raju, "Adaptive Trust Node Routing via Energy-Aware and Steerable Network with Stochastic Optimization for Augmented Intrusion Detection in MANET Infrastructures", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.3, pp. 221-239, 2026. DOI:10.5815/ijcnis.2026.03.12
Reference
[1]S.S.M. Vincent, and N. Duraipandian, Detection and prevention of sinkhole attacks in MANETS based routing protocol using hybrid AdaBoost-Random forest algorithm. Expert Systems with Applications, 249, p.123765, 2024.
[2]P. Aruchamy, S. Gnanaselvi, D. Sowndarya, and P. Naveenkumar, An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks. Concurrency and Computation: Practice and Experience, 35(23), p.e7818, 2023.
[3]B. Sharma, and D. Saxena, Design and Analysis of Energy Efficient Service Discovery Routing Protocol in MANETs. SN Computer Science, 4(5), p.495, 2023.
[4]S. Shafi, S. Mounika, and S.J.P.C.S. Velliangiri, Machine learning and trust based AODV routing protocol to mitigate flooding and blackhole attacks in MANET. Procedia Computer Science, 218, pp.2309-2318, 2023.
[5]R. Sathiya, and N. Yuvaraj, Swarm Optimized Differential Evolution and Probabilistic Extreme Learning based Intrusion Detection in MANET. Computers & Security, p.103970, 2024.
[6]R. Prasad, Secure intrusion detection system routing protocol for mobile ad‐hoc network. Global Transitions Proceedings, 3(2), pp.399-411, 2022.
[7]N. Ilakkiya, and A. Rajaram, Blockchain-enabled lightweight intrusion detection system for secure MANETs. Journal of Electrical Engineering & Technology, 19(4), pp.2667-2681, 2024.
[8]S.M. Hassan, M.M. Mohamad, and F.B. Muchtar, Advanced Intrusion Detection in MANETs: A Survey of Machine Learning and Optimization Techniques for Mitigating Black/Gray Hole Attacks. IEEE Access. 2024.
[9]A.A. Alyoubi, Enhancing Data Security in Mobile Ad-hoc Network (MANETs) Using Trust-Based Approach with RSSI and Fuzzy Logic. Mobile Networks and Applications, pp.1-17, 2024.
[10]M.S. Gowtham, M. Vigenesh, and M. Ramkumar, An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs). Transactions on Emerging Telecommunications Technologies, 35(2), p.e4938, 2024.
[11]R. Chakravorty, J. Prakash, and A. Srivastava, An efficacious neural network and DNA cryptography-based algorithm for preventing black hole attacks in MANET. Soft Computing, 28(5), pp.4667-4679, 2024.
[12]M. Deivakani, M.S. Sheela, K. Priyadarsini, and Y. Farhaoui, An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM). Sustainable Computing: Informatics and Systems, 43, p.101021, 2024.
[13]J. Ryu, and S. Kim, Trust system-and multiple verification technique-based method for detecting wormhole attacks in MANETs. IEEE Access, 2024.
[14]I. Baird, I. Wadhaj, B. Ghaleb, and C. Thomson, Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs). Electronics, 13(16), p.3314, 2024.
[15]S. Rajasoundaran, S.S. Kumar, M. Selvi, K. Thangaramya, and K. Arputharaj, Secure and optimized intrusion detection scheme using LSTM-MAC principles for underwater wireless sensor networks. Wireless Networks, 30(1), pp.209-231, 2024.
[16]S. Sivanesh, and V.R. Sarma Dhulipala, Analytical termination of malicious nodes (ATOM): an intrusion detection system for detecting black hole attack in mobile ad hoc networks. Wireless Personal Communications, pp.1-14, 2022.
[17]S. Selvaraj, and M. Chakkaravarthy, Enhancing Security and efficiency in MANETs: a clustering-based approach with CGRUN and AGTO optimization for intrusion detection and path establishment. International Journal of Information Technology, pp.1-11, 2024.
[18]S. Shafi, and D. Venkata Ratnam, New Energy Aware MPR Selection for Securing OLSR Routing Scheme Under Black Hole Attack: A Machine Learning Approach. Wireless Personal Communications, 132(3), pp.1917-1931, 2023.
[19]N.T. Luong, and D. Hoang, BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks. International Journal of Information Security, 22(6), pp.1547-1566, 2023.
[20]B. Krishnasamy, L. Muthaiah, J.E. Kamali Pushparaj, and P.S. Pandey, DIWGAN optimized with Namib Beetle Optimization Algorithm for intrusion detection in mobile ad hoc networks. IETE Journal of Research, pp.1-20, 2023.
[21]V.A. Devi, V. Ganesan, and V.S.A. Padmini, An Energy Efficient Routing Establishment (EERE) Mechanism for MANET-IoT Security. Franklin Open, p.100150, 2024.
[22]S. Kaushik, K. Tripathi, R. Gupta, and P. Mahajan, Enhancing reliability in mobile ad hoc networks (MANETs) through the K-AOMDV routing protocol to mitigate black hole attacks. SN Computer Science, 5(2), p.263, 2024.
[23]H.L. Gururaj, R. Natarajan, N.A. Almujally, F. Flammini, S. Krishna, and S.K. Gupta, Collaborative energy-efficient routing protocol for sustainable communication in 5G/6G wireless sensor networks. IEEE Open Journal of the Communications Society. 2023.
[24]O.W. Khalid, N.A.M. Isa, and H.A.M. Sakim, Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms. Alexandria Engineering Journal, 63, pp.487-526, 2023.
[25]L. Veefkind, and G. Cesa, A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs. arXiv preprint arXiv:2406.03946. 2024.
[26]L.K. Singh, M. Khanna, H. Garg, and R. Singh, Emperor penguin optimization algorithm-and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images. Soft Computing, 28(3), pp.2431-2467, 2024.