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.

我们提出了一种创新方法，即组合融合，通过使用先进算法将多个机器学习模型相结合，从而提高拒绝服务攻击检测的性能。通过严格的评估，我们证明了这种融合方法的有效性，考虑到精确度、召回率和 F1 分数等指标。我们通过融合模型来解决低调攻击分类的挑战，从而创造出一个全面的解决方案。我们的研究结果强调了这种方法改善拒绝服务攻击检测和增强防御机制的潜力。