Intrusion Detection using Naive Bayes Ant Colony Optimization Algorithm in a Wireless Communication Network
K Padma · Journal of Networking and Communication Systems (JNACS) · 2022
In WCNs, a significant issue is that it possesses the least amount of resources that tend to high-security threats.An Intrusion Detection System (IDS) is a method is used to identify and recognize the attacks.A fuzzy Naïve Bayes Ant Colony Optimization Algorithm system (FNACO) approach is presented in this work for the Intrusion detection model.At first, using the fuzzy clustering model, the dataset is grouped.Moreover, the Naive Bayes classifier is combined with ACO Algorithm that is named NACO is formed to generate optimally the probability measures.Subsequently, the optimization algorithm is used for each data group as well as aggregated data is produced.Subsequent to the aggregated data generation, the optimization technique is used to aggregate data, and on the basis of the posterior probability function, the abnormal nodes are recognized.Finally, the performance analysis is done by comparing the proposed method with the conventional models by exploiting the evaluation measures such as accuracy and False Acceptance Rate (FAR).The outcomes exhibit that adopted model performance is higher than the conventional models that exhibit the superiority of the adopted model in intrusion detection.