Black Hole Attack Detection in Adhoc Networks Using KNN Algorithm with Reputation Calculation

The “black hole” attack is one of many security issues that can affect ad hoc networks. A black hole attack occurs when a hostile node drops incoming packets on purpose, causing a disruption in communication and putting the security of the network at risk. It is essential for the security of ad hoc networks to be able to detect such attacks. In order to identify black hole attacks, this work introduces a novel method based on the K-Nearest Neighbors (KNN) algorithm and a reputation calculation mechanism. Each node in the network is given a reputation score based on its past actions and current performance indicators as the first step in the suggested method. These dynamic reputation scores are indicative of each node's reliability in the network as a whole. Factors including packet forwarding behavior, packet drop rates, delays, and routing behavior are among the relevant features retrieved from network data. The KNN algorithm is used as a classifier, sorting nodes into safe and dangerous categories based on the attributes that have been retrieved. To determine the status of a node, find its K nearest neighbors in the training dataset and average their classifications. When deciding what value of K to use, attention must be taken to maximize detection accuracy while minimizing false positives. Important to this method is determining what percentage of a node's K nearest neighbors must agree in order to label it as malevolent. The results of the classification are then used to revise the user's reputation score. Reputation scores for nodes that have been labeled as harmful may decrease, while those for nodes that have been labeled as normal may climb. Performance measurements such as precision, recall, F1-score, and false positive rate are used to evaluate the accuracy of the detection system in both simulated and real-world tests to determine how effective the proposed method is. Combining reputation-based trust mechanisms with machine learning techniques, this method presents a possible answer to the persistent difficulty of black hole assaults in ad hoc networks. Evidence from real-world deployments shows that it can significantly improve ad hoc network communication security and dependability.