Similarity search aids cybersecurity in autonomous driving by identifying patterns in data that resemble known threats, enabling faster detection of attacks. Autonomous vehicles generate massive streams of data from sensors, communication systems, and software, making manual monitoring impractical. By comparing real-time data to historical examples of malicious activity, similarity search algorithms can flag anomalies that might indicate attacks. For instance, if a sensor input pattern matches a previously observed attack (like spoofed GPS signals), the system can trigger alerts or defensive actions. This approach is efficient because it avoids reinventing detection logic for every new threat, instead leveraging existing knowledge of attack signatures.
A practical example is detecting adversarial attacks on machine learning models used in perception systems. Attackers might manipulate camera or LiDAR data to mislead an autonomous vehicle’s object detection. Similarity search can compare incoming sensor data against a database of known adversarial patterns. For instance, if a distorted image of a stop sign (designed to be misclassified) shares features with a cataloged adversarial example, the system can block the input or revert to a safe state. Similarly, in-vehicle network traffic (like CAN bus messages) can be monitored for sequences that resemble injection attacks—such as sudden, repetitive commands to steer or brake abnormally. By hashing or embedding these messages into vectors, similarity search tools like approximate nearest neighbor (ANN) algorithms can quickly identify matches to malicious payloads.
Another use case is log analysis for intrusion detection. Autonomous vehicles generate logs from software, firmware, and hardware components. Similarity search can scan these logs for sequences that match known attack behaviors, such as unauthorized access attempts or abnormal process executions. For example, if a series of failed authentication attempts in a control module’s logs resembles a brute-force attack pattern, the system can lock down access. Tools like Elasticsearch’s k-nearest neighbors (k-NN) plugin or specialized vector databases (e.g., Milvus) enable efficient comparisons even with high-dimensional data. By focusing on similarity rather than exact matches, this method adapts to variations in attack techniques while reducing false positives compared to rigid rule-based systems. This makes it a scalable layer in a multi-layered defense strategy for autonomous vehicles.