Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis

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.

缓解拒绝服务（DoS）攻击对于在线服务的安全性和可用性至关重要。虽然机器学习（ML）模型被用于检测DoS攻击，但需要新的策略来提高其性能。我们提出一种创新方法——组合融合，它使用先进算法将多个ML模型结合起来。这包括分数和排名组合、加权技术以及评分系统的多样性优势。通过严格的评估，我们证明了这种融合方法的有效性，考虑了准确率、召回率和F1分数等指标。我们通过融合模型来创建一个全面的解决方案，以应对低特征攻击分类的挑战。我们的研究结果强调了这种方法在改进DoS攻击检测以及为更强的防御机制做出贡献方面的潜力。