  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.
