VANET TRAFFIC SIMULATION FOR BLACKHOLE ATTACK DETECTION USING AODV ROUTING PROTOCOL

Abstract

Vehicular Ad-hoc Networks (VANETs) play a pivotal role in modern intelligent transportation systems by enabling seamless Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. However, their dynamic and decentralized nature exposes them to security vulnerabilities, particularly Blackhole attacks, where malicious nodes disrupt network operations by advertising false routes and dropping packets. This study evaluates the impact of Blackhole attacks on VANET performance using the Ad-hoc On-Demand Distance Vector (AODV) routing protocol. Simulations were conducted in NS-2 with BonnMotion mobility models, varying node densities (20–60 nodes) within a 1000×1000 m² area over 140 seconds to emulate urban traffic congestion. Key metrics such as End-to-End Delay (EED), Packet Delivery Ratio (PDR), Throughput, Goodput, and Packet Loss Rate (PLR) were analysed under normal and attack scenarios. Results revealed severe network degradation during attacks: EED surged by 63.43% (from baseline 175.05 ms), PLR exceeded 80% consistently, and PDR plummeted drastically (e.g., from 99.78% to 10.01% for 60 nodes). Throughput declined by up to 85% (e.g., 46.94 Kbps to 6.84 Kbps for 60 nodes), while Goodput exhibited similar deterioration due to malicious packet drops. Notably, higher node density exacerbated congestion and attack impacts, underscoring the vulnerability of scalable VANETs. The findings highlight the Blackhole attack’s crippling effects on data reliability and real-time communication, critical for safety applications like emergency messaging and traffic management. This study underscores the urgent need for robust mitigation strategies, including trust-based protocols, intrusion detection systems, and adaptive routing algorithms, to safeguard VANETs against such threats. By addressing these vulnerabilities, this research advances secure, efficient vehicular communication frameworks, ensuring the operational integrity and safety of future intelligent transportation ecosystems.