  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.
