A Trust Management Scheme for Intrusion Detection System in MANET using Weighted Naïve Bayes Classifier
How to Cite?
Fouziah Hamza, S. Maria Celestin Vigila, "A Trust Management Scheme for Intrusion Detection System in MANET using Weighted Naïve Bayes Classifier," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 95-107, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P211
The primary research considerations are the development of intrusion detection and preventing mobile ad hoc networks (MANET) techniques with an exact detection rate and energy consumption with low packet loss. Node energy and node mobility are two major optimization challenges in MANETs, in which nodes move insecurely in all directions, and the topology is constantly changing. A significant clustering is carried out by the Emperor Penguin Optimization (EPO) algorithm. The cluster head selection is processed using a fuzzy strategy with a genetic algorithm (GA). The motive of this work is to use a trust management method based on DempsterShafer (D-S) evidence theory to identify intrusion behaviour. In addition, the attack pattern classification using the weighted Naive-Bayes method reduces the complexity of the classification. The features are extracted from the recognized pattern and finally transferred to the classifier for classification. Learning complexities can be overcome during the classification process using the Social Spider Optimization (SSO) technique. The proposed mathematical model detects the intrusion based on the final trust score. The robustness of the proposed model is executed based on attack detection rate, energy usage, and throughput for detecting and isolating the intruder. According to simulation results, the proposed solution effectively reduces IDS traffic and overall energy usage while maintaining a high attack detection rate and throughput.
Intrusion detection system (IDS), clustering, cluster head election, classification, weight optimization, security.
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