Similarity search plays a critical role in intrusion detection systems (IDS) for autonomous vehicles by enabling efficient comparison of real-time data against known patterns of normal behavior or historical attack signatures. Autonomous vehicles generate vast amounts of data from sensors, cameras, and communication systems, making it challenging to detect anomalies in real time. Similarity search algorithms, such as k-nearest neighbors (k-NN) or approximate nearest neighbor (ANN) methods, allow the IDS to quickly identify deviations by measuring how closely current data matches predefined safe patterns. For example, if a vehicle’s controller area network (CAN bus) traffic suddenly includes unusual message frequencies or payloads, similarity search can flag these as potential intrusions by comparing them to a baseline of legitimate traffic. This approach reduces false positives by focusing on meaningful deviations rather than arbitrary thresholds.
A practical example involves detecting replay attacks, where an attacker resends valid CAN bus messages to trick the vehicle. Traditional signature-based detection might miss this if the messages themselves are legitimate. However, similarity search can analyze the temporal context—such as the timing between messages—to identify abnormal repetition patterns. By indexing normal message sequences, the system can detect when incoming data diverges from expected behavior. Similarly, in sensor fusion systems, similarity search helps correlate data across LiDAR, radar, and cameras. If a sensor’s output suddenly becomes inconsistent with others (e.g., a camera feed showing a clear road while LiDAR detects an obstacle), the IDS can flag this mismatch by comparing it to historical scenarios where sensors agreed.
For developers, implementing similarity search in IDS requires balancing speed and accuracy. High-dimensional data from autonomous vehicles (e.g., sensor readings, network logs) can strain traditional search methods. Tools like FAISS or ANNOY optimize this by using vector indexing to enable fast approximate matches. Feature engineering is also critical: raw data must be transformed into embeddings that capture relevant patterns (e.g., using autoencoders to represent CAN bus traffic). However, challenges remain, such as handling concept drift (gradual changes in normal behavior over time) and ensuring low-latency processing. By integrating similarity search with machine learning models, developers can create adaptive IDS that evolve with new threats while maintaining real-time performance—a key requirement for autonomous vehicle safety.