A Hybrid Approach Against Black Hole Attackers Using Dynamic Threshold Value and Node Credibility

Detecting black hole attackers is tedious in Vehicular Ad Hoc Networks due to vehicles’ high mobility. The main consequence faced because of these attackers is an increase in the number of dropped packets which converts secure and fastest paths to compromised ones. Since these attackers can act individually and collaboratively as a group, early detection of these attackers must be feasible to preserve the network’s performance. The majority of current methods rely on predetermined threshold and trust score values, which are ineffective in accurately identifying black hole attackers. Hence, this paper proposes a hybrid approach using dynamic threshold value and node credibility for early detection of black hole attackers. RSUs periodically compute the dynamic threshold value and categorize the vehicles into categories 1, 2, and 3. Vehicles classified as Category 1 are legitimate, whereas Category 3 vehicles are attackers. Vehicles in Category 2 are suspicious, requiring further analysis using node credibility values to identify attackers. It is protected against single, multiple, and collaborative black hole attackers. The NS2 simulation results demonstrate that the suggested method is optimal concerning PDR, Throughput, Delay, and Packet Loss Ratio compared to recent techniques. Since the proposed scheme efficiently identifies the attackers, it has 89.67% PDR, which is higher when compared to other schemes.