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

Osanaiye, O; Cai, H; Choo, KR; Dehghantanha, A; Xu, Z; Dlodlo, M

Abstract

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.

Citation

Osanaiye, O., Cai, H., Choo, K., Dehghantanha, A., Xu, Z., & Dlodlo, M. (2016). Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP Journal on Wireless Communications and Networking, 130, https://doi.org/10.1186/s13638-016-0623-3

• Journal Article Type: Article
• Acceptance Date: Apr 24, 2016
• Online Publication Date: May 10, 2016
• Publication Date: Dec 1, 2016
• Deposit Date: May 31, 2016
• Publicly Available Date: May 31, 2016
• Journal: EURASIP Journal on Wireless Communications
• Print ISSN: 1687-1472
• Publisher: Springer Verlag
• Volume: 130
• DOI: https://doi.org/10.1186/s13638-016-0623-3
• Publisher URL: http://dx.doi.org/10.1186/s13638-016-0623-3
• Related Public URLs: http://link.springer.com/journal/13638
• Additional Information: Projects : A Formal Rule-Processing Engine for Privacy-Respecting Forensic Investigation; Grant Number: 625402