Enhanced Anomaly Detection in Automotive Systems Using SAAD: Statistical Aggregated Anomaly Detection

TL;DR

This paper introduces SAAD, a novel anomaly detection method combining statistical techniques and machine learning, significantly improving detection accuracy in automotive sensor data over individual models.

Contribution

The paper presents SAAD, a new integrated anomaly detection approach that enhances accuracy and robustness by combining statistical methods with deep learning in automotive systems.

Findings

SAAD achieves 88.3% accuracy, outperforming individual models.

The combined approach yields an F1 score of 0.921.

Statistical method alone reaches 72.1% accuracy.

Abstract

This paper presents a novel anomaly detection methodology termed Statistical Aggregated Anomaly Detection (SAAD). The SAAD approach integrates advanced statistical techniques with machine learning, and its efficacy is demonstrated through validation on real sensor data from a Hardware-in-the-Loop (HIL) environment within the automotive domain. The key innovation of SAAD lies in its ability to significantly enhance the accuracy and robustness of anomaly detection when combined with Fully Connected Networks (FCNs) augmented by dropout layers. Comprehensive experimental evaluations indicate that the standalone statistical method achieves an accuracy of 72.1%, whereas the deep learning model alone attains an accuracy of 71.5%. In contrast, the aggregated method achieves a superior accuracy of 88.3% and an F1 score of 0.921, thereby outperforming the individual models. These results underscore the effectiveness of SAAD, demonstrating its potential for broad application in various domains, including automotive systems.