Voting based Intrusion Detection Framework for Securing Software-Defined
Networks
Abstract
Software-defined networking (SDN) is an emerging paradigm in enterprise
networks because of its flexible and cost-effective nature. By
decoupling control and data plane, SDN can provide various defense
solutions for securing futuristic networks. However, the architectural
design and characteristics of SDN attract several severe attacks.
Distributed Denial of Service (DDoS) is considered as a major
destructive cyber attack that makes the services of controller
unavailable for its legitimate users. In this research paper, an
intrusion detection framework is proposed to detect DDoS attacks against
SDN. The proposed framework relies on voting based ensemble model for
the attack detection. Ensemble model is a combination of multiple
machine learning classifiers for prediction of final results. In this
research paper, we propose and analyze three ensemble models named as
Voting-CMN, Voting-RKM, and Voting-CKM particularly to benchmarking
datasets like UNSW-NB15, CICIDS2017, and NSL-KDD, respectively. For
validation of the proposed models, a cross validation technique is used
with the prediction algorithms. The effectiveness of proposed models is
evaluated in terms of prominent metrics (accuracy, precision, recall,
and F measure). Experimental results indicate that the proposed
models achieve better performance in terms of accuracy as compared to
other existing models.