Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems

TL;DR

This paper investigates how feature importance and explainability can improve machine learning-based detection of DoS attacks in AI systems, emphasizing statistical analysis and feature engineering to enhance detection accuracy.

Contribution

It introduces a detailed analysis of feature contribution using statistical and engineering methods to optimize feature selection for DoS attack detection.

Findings

Statistical analysis reveals key features for DoS detection.

Feature engineering improves model performance.

Optimal feature selection enhances detection accuracy.

Abstract

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.