Similarity search helps detect potential cyber threats in self-driving systems by identifying patterns in data that resemble known malicious activities or anomalies. Self-driving systems rely on vast amounts of sensor data, communication between components, and machine learning models to operate. These systems are vulnerable to attacks like sensor spoofing, adversarial machine learning inputs, or compromised vehicle-to-everything (V2X) communications. By comparing incoming data or system behaviors against a database of known threats or baseline “normal” operations, similarity search flags deviations that could indicate an attack. For example, if a sensor starts sending slightly altered data that matches the pattern of a known spoofing attack, the system can trigger an alert or mitigation steps.
A practical example involves detecting adversarial attacks on machine learning models used for object recognition. Attackers might manipulate images (e.g., adding subtle noise to stop signs to misclassify them). A similarity search engine could compare incoming image feature vectors against a library of known adversarial examples. Even if the exact attack hasn’t been seen before, the system might detect that the input shares characteristics with other manipulated data, such as unusual edge patterns or color distributions. Similarly, in vehicle networks, a sudden influx of messages resembling past denial-of-service (DoS) attack patterns—like abnormal timing or payload sizes—could be flagged using similarity metrics. This approach reduces reliance on exact signature matches, which might miss novel but related threats.
Implementing similarity search requires creating efficient representations of data (like embeddings or hash-based fingerprints) to enable fast comparisons. For instance, time-series data from LiDAR or radar sensors can be converted into feature vectors capturing frequency, amplitude, or spatial patterns. Tools like approximate nearest neighbor (ANN) algorithms (e.g., FAISS or ANNOY) enable real-time searches across large datasets. However, challenges include balancing accuracy with computational speed and ensuring the threat database stays updated. By integrating similarity search into intrusion detection pipelines, developers can build self-driving systems that adapt to evolving threats without requiring manual rule updates, making defenses more proactive and scalable.