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|Author:||Pegah Golchin, Ralf Kundel, Tim Steuer, Rhaban Hark, Ralf Steinmetz|
|Kind:||In proceedings - use for conference & workshop papers|
|Book title:||Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), 2022.|
|Abstract:||DDoS attack detection is crucial in computer networks to meet the reliability and accessibility requirements of online services. The ability of machine learning to discriminate between DDoS attacks and benign flows makes it a promising candidate for DDoS detection. Correctly classifying the flows with high performance in near real-time is a critical issue for an ML-based DDoS detector to reduce the damages of DDoS attacks. In order to improve the performance of classification and reduce the prediction time, we propose a multi-aspect Ensemble Feature Selection (EFS) for DDoS attack detection in this work. The presented EFS selects the most relevant features of each attack separately, leveraging a combination of statistical filtering approaches and machine learning methods. We evaluate our method on two different datasets to demonstrate the EFS robustness toward model-specific biases. Last, we demonstrate that the prediction time is reduced leveraging the proposed EFS.|
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