Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing




Osanaiye, Opeyemi
Cai, Haibin
Choo, Kim-Kwang Raymond
Dehghantanha, Ali
Xu, Zheng
Dlodlo, Mqhele

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Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.



ensemble-based multi-filter feature selection method, filter methods, Cloud DDoS, intrusion detection system, machine learning



Information Systems and Cyber Security