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

arXiv: https://arxiv.org/abs/2407.05461 · HTML: https://mlsanity.com/paper/2407.05461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification](https://mlsanity.com/paper/2002.10061/) (2020)
- [One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes](https://mlsanity.com/paper/2305.12316/) (2023)
- [1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data](https://mlsanity.com/paper/2003.00476/) (2020)
- [Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation](https://mlsanity.com/paper/2309.01828/) (2023)
- [A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles](https://mlsanity.com/paper/1903.08712/) (2019)
- [Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning](https://mlsanity.com/paper/2401.15541/) (2024)

## Built on by (most cited first, 1 of 1)

- [Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safety](https://mlsanity.com/paper/2512.08862/) (2025)

## Abstract (arXiv)

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.