Black Hole Attack Detection Using Dynamic Threshold Values and Residual Energy in VANETs

In black hole attack, a hostile vehicle or node within the VANET pretends to have the optimal route to the destination, convincing other nodes to route their data packets through it as they head towards the destination, trailing behind the malicious node, receives the data, it either discards the packets completely or forwards them selectively, causing disruptions in communication. To overcome this dilemma the research discusses a novel approach for categorizing nodes in AODV (Adhoc On demand vector Routing Protocol) networks according to their residual energy (RE) and forward rate (FR). With the goal to identify nodes that are considered energy-constrained, the proposed approach defines a residual energy threshold (RET) and includes dynamic threshold values (MinDTV and MaxDTV) for the forward rate. Three categories are applied to nodes: 1. (Trusted), 2. (Intermediate), and 3. (Untrusted or Energy-Constrained). This methodology results in a more trustworthy and energy-efficient network by dynamically updating node selection based on present forwarding behavior and levels of energy. The proposed approach boosts the overall strength, longevity as well, and integrity of RPL networks through the elimination of energy-depleted and unstable nodes; hence, it is especially suitable for applications in scenarios with scarce resources and malicious activity.