Creating A Realistic Sybil Attack Dataset For Inter-vehicle Communication
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A Sybil attack, in which a malicious node uses multiple identities simultaneously to deceive other participants, poses a significant threat to Vehicular Ad-Hoc Networks (VANETs). Traditional solutions used in conventional networks are often ineffective in VANET due to the network's dynamic nature and the strict timing constraints of safety-critical applications. An alternative approach involves leveraging physical attributes of received messages, such as the Received Signal Strength Indicator (RSSI), for attack detection. However, research on RSSI-based Sybil attack detection in VANET is limited by a lack of realistic datasets. This work aims to bridge this gap by creating a simulation environment that accurately reflects real-world road structures, traffic flows, and environmental factors. We began by importing a region of Istanbul's historical center from OpenStreetMap. Next, we designed a traffic scenario replicating real-world density based on municipal data, incorporating packet loss due to channel capacity constraints and signal interference. We carefully calibrated the radio propagation model to accurately reflect the impacts of both surrounding landscapes and signal interference on RSSI readings. We implemented four well-known sybil attack methods with five power control options, yielding 20 distinct attack scenarios. Our dataset was rigorously validated against traffic flow, RSSI measurements, attack distribution, and packet collisions. Finally, we established a portable data format and released a sample dataset, along with our simulation environment's source, settings, and scenarios.