Distributed Denial of Service (DDoS) attacks are one of the most important security threats, since multiple compromised systems perform massive attacks over a victim, overwhelming its bandwidth and/or resources. Such attacks can be detected, for example, by using supervised machine learning based solutions previously trained on large DDoS attack datasets in order to automatically identify malicious patterns present in the incoming traffic. In addition, since large datasets show inherent multidimensional structures, tensor based detection techniques can outperform the matrix based counterparts. In this context, the development of a DDoS attack detection framework which exploits both machine learning and tensor based approaches is crucial. To face this challenge, this paper proposes a novel tensor based framework for DDoS attack detection using concepts of multiple denoising, tensor decomposition and machine learning supervised classification. Moreover, we also propose an extension of the recent Multiple Denoising algorithm such that the noise present in the dataset instances is more efficiently attenuated. Finally, we validate the effectiveness of our proposed framework through comparison with state-of-the-art low-rank approximation techniques as well as with related works. The proposed approach outperforms its competitor schemes in terms of accuracy, detection rate and false alarm rate.

分布式拒绝服务（DDoS）攻击是最重要的安全威胁之一，因为多个受感染的系统对受害者进行了大规模攻击，从而淹没了其带宽和/或资源。例如，可以通过使用基于监督的机器学习的解决方案来检测此类攻击，这些解决方案以前是在大型DDoS攻击数据集上进行训练的，以便自动识别传入流量中存在的恶意模式。另外，由于大型数据集显示出固有的多维结构，因此基于张量的检测技术可以胜过基于矩阵的对应物。在这种情况下，开发利用机器学习和基于张量的方法的DDoS攻击检测框架至关重要。面对挑战，本文提出了一种新颖的基于张量的DDoS攻击检测框架，该框架使用多重降噪，张量分解和机器学习监督分类的概念。此外，我们还提出了对最近的多重降噪算法的扩展，以使数据集实例中存在的噪声得到更有效的衰减。最后，我们通过与最新的低秩逼近技术以及相关工作进行比较，验证了我们提出的框架的有效性。提出的方法在准确性，检测率和误报率方面优于其竞争对手方案。我们还建议对最近的多重降噪算法进行扩展，以使数据集实例中存在的噪声得到更有效的衰减。最后，我们通过与最新的低秩逼近技术以及相关工作进行比较，验证了我们提出的框架的有效性。提出的方法在准确性，检测率和误报率方面优于其竞争对手方案。我们还建议对最近的多重降噪算法进行扩展，以使数据集实例中存在的噪声得到更有效的衰减。最后，我们通过与最新的低秩逼近技术以及相关工作进行比较，验证了我们提出的框架的有效性。提出的方法在准确性，检测率和误报率方面优于其竞争对手方案。