Distributed Denial of Service (DDoS) attacks are one of the most challenging security threats, since a single victim is attacked by several compromised malicious nodes. As a consequence, legitimate end users can be prevented to access network resources. This letter proposes a noise-robust multilayer perceptron (MLP) architecture for DDoS attack detection trained with corrupted data. In the proposed approach, the average value of the common features among dataset instances is iteratively filtered out by applying Higher Order Singular Value Decomposition (HOSVD) based techniques. The effectiveness of the proposed architecture is validated through comparison with state-of-the-art methods.

中文翻译：

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用于分布式拒绝服务攻击检测的强噪声多层感知器体系结构

分布式拒绝服务（DDoS）攻击是最具挑战性的安全威胁之一，因为单个受害者受到数个受损的恶意节点的攻击。结果，可以防止合法的最终用户访问网络资源。这封信提出了一种针对经过损坏数据训练的DDoS攻击检测的鲁棒性多层感知器（MLP）架构。在提出的方法中，通过应用基于高阶奇异值分解（HOSVD）的技术，迭代地滤除数据集实例之间的公共特征的平均值。通过与最先进的方法进行比较，验证了所提出体系结构的有效性。