CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks

TL;DR

This paper introduces CAV-AD, a comprehensive framework combining a novel CNN architecture and filtering techniques to detect multiple anomalies and identify malicious sensors in CAV networks with high accuracy.

Contribution

The paper presents a new anomaly detection framework, CAV-AD, that effectively detects multiple anomalies and identifies malicious sensors in CAV networks, outperforming existing methods.

Findings

Achieves 98% accuracy in anomaly detection.

Attains 89% F1 score in identifying malicious sensors.

Outperforms state-of-the-art methods in experiments.

Abstract

The adoption of connected and automated vehicles (CAVs) has sparked considerable interest across diverse industries, including public transportation, underground mining, and agriculture sectors. However, CAVs' reliance on sensor readings makes them vulnerable to significant threats. Manipulating these readings can compromise CAV network security, posing serious risks for malicious activities. Although several anomaly detection (AD) approaches for CAV networks are proposed, they often fail to: i) detect multiple anomalies in specific sensor(s) with high accuracy or F1 score, and ii) identify the specific sensor being attacked. In response, this paper proposes a novel framework tailored to CAV networks, called CAV-AD, for distinguishing abnormal readings amidst multiple anomaly data while identifying malicious sensors. Specifically, CAV-AD comprises two main components: i) A novel CNN model architecture called optimized omni-scale CNN (O-OS-CNN), which optimally selects the time scale by generating all possible kernel sizes for input time series data; ii) An amplification block to increase the values of anomaly readings, enhancing sensitivity for detecting anomalies. Not only that, but CAV-AD integrates the proposed O-OS-CNN with a Kalman filter to instantly identify the malicious sensors. We extensively train CAV-AD using real-world datasets containing both instant and constant attacks, evaluating its performance in detecting intrusions from multiple anomalies, which presents a more challenging scenario. Our results demonstrate that CAV-AD outperforms state-of-the-art methods, achieving an average accuracy of 98% and an average F1 score of 89\%, while accurately identifying the malicious sensors.