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Intrusion detection in software defined networks with imbalanced attack classes
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Authors: Sotiris Chatzimiltis, Suraj Rohira Lucas, Mohammad Shojafar, Mahdi Boloursaz Mashhadi, Rahim Tafazolli
Status: Final
Date of publication: 10 December 2024
Published in: ITU Journal on Future and Evolving Technologies, Volume 5 (2024), Issue 4, Pages 422-432
Article DOI : https://doi.org/10.52953/UQWK9413
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Abstract:
Software Defined Networks (SDNs) have revolutionized the way modern networks are managed and orchestrated. This sophisticated infrastructure can provide numerous benefits but at the same time introduce several security challenges. A centralized controller holds the responsibility of managing the network traffic, thus making it an attractive target to attackers. Intrusion Detection Systems (IDSs) play a crucial role in identifying and addressing security threats within the SDN. In this paper, we developed an SDN-IDS system by utilizing machine learning techniques for anomaly detection to identify deviations in network behavior. This is specifically challenging due to the fact that we only have a few samples from several of the attack classes, i.e. minority classes. Five machine learning algorithms were employed to train the SDN-IDS, and ultimately the most appropriate one was chosen. Moreover, we applied the SMOTE and Tomek link re-samplings on the dataset as well as a cost-sensitive learning technique to enhance the classification performance of the minority attacks. The Decision Tree (DT) model, trained on a feature-reduced and resampled dataset using cost-sensitive learning, achieved an impressive overall performance with 99.87% accuracy and an F1-score of 99.87. Additionally, it demonstrated a classification accuracy above 99% in identifying 11 out of the 15 possible traffic classes. |
Keywords: Imbalanced data, intrusion detection system, machine learning, software defined network
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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