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.

翻译：拒绝服务（DoS）攻击在AI系统安全领域构成重大威胁，导致巨大的经济损失和系统停机。然而，AI系统的高计算需求、动态行为和数据可变性使得监控和检测DoS攻击具有挑战性。目前，基于统计和机器学习（ML）的DoS分类与检测方法利用广泛的特征选择机制从网络流量数据集中选择特征子集。特征选择对于提升整体模型性能、提高攻击检测准确率以及减少训练时间至关重要。本文研究了特征选择在改进基于机器学习的DoS攻击检测中的重要性。具体而言，我们通过统计分析方法和特征工程技术，探究了特征对DoS流量数据集中各组成部分的贡献。我们的实验结果表明，对DoS流量进行深入的统计分析并结合特征工程，有助于理解攻击行为，并为基于机器学习的DoS分类与检测确定最佳特征选择方案。