Similarity search enhances vehicle-to-vehicle (V2V) communication security by enabling systems to detect anomalies, verify message authenticity, and identify malicious actors through pattern recognition. In V2V networks, vehicles constantly exchange data like location, speed, and sensor readings to coordinate actions. However, attackers could spoof messages or inject false data to disrupt traffic. Similarity search algorithms analyze incoming messages against historical or expected behavior patterns to flag inconsistencies. For example, if a vehicle’s reported position or acceleration deviates sharply from its typical behavior—or from nearby vehicles—the system can trigger alerts or block suspicious messages. This adds a layer of security beyond traditional methods like encryption or digital signatures, which alone cannot detect spoofed but cryptographically valid data.
A practical example involves validating trajectory data. Vehicles in a network might share their planned routes and real-time positions. Using similarity search, a receiver can compare a vehicle’s current trajectory to its historical routes (e.g., frequent commutes) or to the paths of neighboring vehicles. If a message claims a sudden, improbable turn that doesn’t align with these patterns, the system can flag it as potentially fraudulent. Similarly, similarity search can detect Sybil attacks—where a malicious vehicle impersonates multiple identities—by identifying messages with overlapping or implausibly similar metadata (e.g., timestamps, geolocation). For instance, two messages from “different” vehicles that share identical sensor noise patterns or timing intervals could indicate a single attacker generating fake data.
Implementing similarity search requires defining features for comparison and optimizing for real-time performance. Developers might extract features like message frequency, geospatial coordinates, or sensor signatures (e.g., acceleration curves) and store them in a database optimized for fast nearest-neighbor queries, such as a vector database. When a new message arrives, its features are compared to recent entries using metrics like cosine similarity or Euclidean distance. To reduce latency, techniques like locality-sensitive hashing (LSH) or approximate nearest-neighbor (ANN) algorithms can prioritize speed over perfect accuracy. For example, a vehicle receiving a “hard brake” alert could cross-check the sender’s current speed pattern against a subset of recent messages from nearby vehicles to validate consistency. This approach complements cryptographic methods, creating a multi-layered defense against both data tampering and sophisticated spoofing attacks.