Proposed Naïve Bayes- Genetic algorithm to detect black hole attacks in MANETs.

The threats and attacks on mobile ad hoc networks (MANETs) are significant, making it difficult for traditional security systems to provide complete protection. Therefore, an efficient hybrid clustering approach must be designed to construct an intrusion detection system (IDS) that is suitable for this network. IDSs are crucial in MANETs due to the presence of black hole threats, which are the most significant vulnerabilities in this network.Our suggestion is to use a hybrid classifier to detect black hole attacks in MANETs. This can be achieved by using the Naïve Bayes algorithm for clustering to select the cluster head, and modify the Genetic Algorithm to identify the node responsible for a black hole attack on the optimal path. Finally, the confidence server instructs the destination node. If permitted, it alerts the collection head; otherwise, it identifies the node as a malicious one in the black hole attack within each cluster. The results of our proposed technique show that it has improved package damage rate, quantity, package distribution ratio, whole network interruption, and standardized directing capacity parameters compared to current black hole detection approaches. The simulation was done in KDD cup 99 to carry out black hole attack and trace file obtained is used as dataset for training and testing purpose using visual basic.