Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis

Authors: Evans Owusu, Mohamed Rahouti, D. Frank Hsu, Kaiqi Xiong, Yufeng Xin
Published: 2023-10-02

Source: https://arxiv.org/abs/2312.00006

PDF: https://arxiv.org/pdf/2312.00006

AIにより推定されたラベル

モデル性能評価 スタッキングモデル DoS対策

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability. While machine learning (ML) models are used for DoS attack detection, new strategies are needed to enhance their performance. We suggest an innovative method, combinatorial fusion, which combines multiple ML models using advanced algorithms. This includes score and rank combinations, weighted techniques, and diversity strength of scoring systems. Through rigorous evaluations, we demonstrate the effectiveness of this fusion approach, considering metrics like precision, recall, and F1-score. We address the challenge of low-profiled attack classification by fusing models to create a comprehensive solution. Our findings emphasize the potential of this approach to improve DoS attack detection and contribute to stronger defense mechanisms.