Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems
This work addresses DoS attack detection for AI system security, but it is incremental as it builds on existing feature selection approaches.
The paper tackles the challenge of detecting Denial of Service (DoS) attacks in AI systems by investigating feature selection methods, finding that statistical analysis and feature engineering improve ML-based detection accuracy and reduce training time.
Denial of Service (DoS) attacks pose a significant threat in the realm of AI systems security, causing substantial financial losses and downtime. However, AI systems' high computational demands, dynamic behavior, and data variability make monitoring and detecting DoS attacks challenging. Nowadays, statistical and machine learning (ML)-based DoS classification and detection approaches utilize a broad range of feature selection mechanisms to select a feature subset from networking traffic datasets. Feature selection is critical in enhancing the overall model performance and attack detection accuracy while reducing the training time. In this paper, we investigate the importance of feature selection in improving ML-based detection of DoS attacks. Specifically, we explore feature contribution to the overall components in DoS traffic datasets by utilizing statistical analysis and feature engineering approaches. Our experimental findings demonstrate the usefulness of the thorough statistical analysis of DoS traffic and feature engineering in understanding the behavior of the attack and identifying the best feature selection for ML-based DoS classification and detection.