Self-driving cars can use vector search to detect new cyber threats by analyzing patterns in data and identifying anomalies that deviate from normal behavior. Vector search works by converting data into numerical representations (vectors) and comparing them in a high-dimensional space. For self-driving cars, this means translating network traffic, sensor outputs, or software behavior into vectors, then using a database to quickly search for similarities or outliers. When a threat is detected, the system can flag it for further analysis or mitigation, even if it doesn’t match known attack signatures.
For example, a self-driving car’s internal network might generate logs from sensors, cameras, and control systems. These logs can be processed into vectors using machine learning models like autoencoders or transformers, which capture the relationships between data points. If an attacker attempts to inject malicious data into a LiDAR sensor, the resulting vector might differ significantly from typical sensor outputs. A vector database (e.g., FAISS or Milvus) could index normal behavior vectors and perform nearest-neighbor searches to detect deviations. This approach is especially useful for zero-day threats, where traditional signature-based detection falls short. Additionally, vector search can correlate data across subsystems—like linking unusual GPS data with erratic steering commands—to identify multi-vector attacks that might otherwise go unnoticed.
To implement this, developers would first train models to generate meaningful vectors from the car’s operational data. For instance, network packets could be embedded based on metadata (source, size, frequency) and payload patterns. These vectors are then indexed in a search-optimized database. During operation, real-time data is converted to vectors and queried against the index. If a vector’s distance from its nearest neighbors exceeds a threshold, it’s flagged as suspicious. Over time, the system can adapt by adding confirmed threat vectors to the database, improving detection accuracy. This method also scales efficiently, as vector search algorithms are designed for low-latency queries, critical for real-time systems like autonomous vehicles. By combining vector search with traditional security layers (firewalls, encryption), developers create a robust defense against evolving cyber threats.