Anomaly-Based Intrusion Detection for Detecting Blackhole Attack Mitigataion

In the contemporary environment, mobile ad hoc networks (MANETs) are becoming necessary. They are absolutely vital in a variety of situations where setting up a network quickly is required; however, this is infeasible due to low resources. Ad hoc networks have many applications: education, on the front lines of battle, rescue missions, etc. These networks are distinguished by high mobility and constrained compute, storage, and energy capabilities. As a result of a lack of infrastructure, they do not use communication tools related to infrastructure. Instead, these networks rely on one another for routing and communication. Each node in a MANET searches for another node within its communication range and uses it as a hop to relay the message through a subsequent node, and so on. Traditional networks have routers, servers, firewalls, and specialized hardware. In contrast, each node in ad hoc networks has multiple functions. Nodes, for instance, manage the routing operation. Consequently, they are more vulnerable to attacks than traditional networks. This study's main goal is to develop an approach for detecting blackhole attacks using anomaly detection based on Support Vector Machine (SVM). This detection system looks at node activity to scan network traffic for irregularities. In blackhole scenarios, attacking nodes have distinct behavioral characteristics that distinguish them from other nodes. These traits can be efficiently detected by the proposed SVM-based detection system. To evaluate the effectiveness of this approach, traffic under blackhole attack is created using the OMNET++ simulator. Based on the categorization of the traffic into malicious and non-malicious, the malicious node is then identified. The results of the suggested approach show great accuracy in detecting blackhole attacks.