One of the most common internet attacks causing significant economic losses in recent years is the Denial of Service (DoS) flooding attack. As a countermeasure, intrusion detection systems equipped with machine learning classification algorithms were developed to detect anomalies in network traffic. These classification algorithms had varying degrees of success, depending on the type of DoS attack used. In this paper, we use an SNMP-MIB dataset from real testbed to explore the most prominent DoS attacks and the chances of their detection based on the classification algorithm used. The results show that most DOS attacks used nowadays can be detected with high accuracy using machine learning classification techniques based on features provided by SNMP-MIB. We also conclude that of all the attacks we studied, the Slowloris attack had the highest detection rate, on the other hand TCP-SYN had the lowest detection rate throughout all classification techniques, despite being one of the most used DoS attacks.

中文翻译：

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使用多分类器的基于攻击的 DoS 攻击检测

近年来造成重大经济损失的最常见的互联网攻击之一是拒绝服务 (DoS) 泛洪攻击。作为对策，开发了配备机器学习分类算法的入侵检测系统来检测网络流量中的异常。这些分类算法取得了不同程度的成功，具体取决于所使用的 DoS 攻击类型。在本文中，我们使用来自真实测试平台的 SNMP-MIB 数据集来探索最突出的 DoS 攻击及其基于使用的分类算法被检测到的机会。结果表明，使用基于 SNMP-MIB 提供的特征的机器学习分类技术，可以高精度检测当今使用的大多数 DOS 攻击。我们还得出结论，在我们研究的所有攻击中，