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

Autor:	Yakubu, Paul Badu, Owusu, Evans, Santana, Lesther, Rahouti, Mohamed, Chehri, Abdellah, Xiong, Kaiqi
Rok vydání:	2024
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 2 figures, IEEE VTC2024-Fall
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.03355 Zobrazit plný text záznamu View this record from Arxiv