Fast Two-Stage Intrusion Detection System Developed for Connected Autonomous Vehicles

Global: Fast Two-Stage Intrusion Detection System Developed for Connected Autonomous Vehicles

Researchers led by Devika S, along with Vishnu Hari, Pratik Narang, Tejasvi Alladi, and Vinay Chamola, announced a new intrusion detection system (IDS) tailored for connected and autonomous vehicles (CAVs) in a paper submitted to arXiv on 30 Dec 2025. The work, titled FAST-IDS, aims to provide real‑time threat detection while operating within the limited computational resources typical of vehicular environments.

System Architecture

FAST-IDS employs a two‑stage design that first filters network traffic using lightweight heuristics before invoking a more sophisticated detection model. This staged approach is intended to reduce processing overhead and prioritize suspicious flows for deeper analysis.

Hybrid Model Compression

The authors report the use of hybrid compression techniques that combine pruning, quantization, and knowledge distillation to shrink the underlying machine‑learning models. According to the abstract, the compressed models retain detection accuracy while fitting the memory and power constraints of on‑board vehicle hardware.

Real‑Time Performance

Preliminary results suggest that the system can operate at line‑rate speeds, enabling immediate identification of malicious activity without introducing noticeable latency to vehicle communication channels. The paper notes that the implementation was tested on a 1,426 KB codebase, reflecting a focus on compact deployment.

Implications for Vehicle Security

By targeting CAVs, FAST-IDS addresses a growing concern in the automotive sector where increasing connectivity expands the attack surface. The authors contend that a fast, resource‑efficient IDS could enhance the resilience of autonomous driving platforms against emerging cyber threats.

Next Steps

Future research outlined by the team includes extensive validation on real‑world vehicular datasets and integration with existing automotive safety standards. The authors also plan to explore adaptive compression strategies that respond to evolving threat landscapes.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.