A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks

Haddad Pajouh, H; Javadian, R; Khayami, R; Dehghantanha, A; Choo, R

Abstract

With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naïve Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks.

Citation

Haddad Pajouh, H., Javadian, R., Khayami, R., Dehghantanha, A., & Choo, R. (2019). A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Transactions on Emerging Topics in Computing, 7(2), 314-323. https://doi.org/10.1109/TETC.2016.2633228

• Journal Article Type: Article
• Acceptance Date: Nov 21, 2016
• Online Publication Date: Nov 29, 2016
• Publication Date: Jun 5, 2019
• Deposit Date: Dec 12, 2016
• Publicly Available Date: Dec 12, 2016
• Journal: IEEE Transactions on Emerging Topics in Computing
• Print ISSN: 2168-6750
• Electronic ISSN: 2168-6750
• Publisher: Institute of Electrical and Electronics Engineers
• Volume: 7
• Issue: 2
• Pages: 314-323
• DOI: https://doi.org/10.1109/TETC.2016.2633228
• Publisher URL: http://dx.doi.org/10.1109/TETC.2016.2633228
• Related Public URLs: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6245516
• Additional Information: Projects : Privacy4Forensic