With growing web communications throughout the Internet, the need for better security protection has been intensified. Intrusion detection is important for identifying malicious activities and cyber-crimes. Recently, UNSW-NB15 dataset has been popular in the research community for network intrusion detection system as it is publicly available labelled dataset which has a hybrid of real normal and contemporary synthesized attack activities of the network traffic. The goal of this paper is to relabel an unsupervised labelled data using a hybrid approach based on supervised learning. The methodology for training model in this work includes: (i) feature selection to remove redundant and highly correlated features, (ii) clustering the training dataset to create referential labels based on the size of cluster by using selected features, (iii) creating supervised learning model using ensemble classifiers with the generated referential labels, and (iv) testing individual data-point in doubt using the generated learning model. Our results show 81.29% accuracy compared to the original labels. Further, the proposed ensemble technique using LogitBoost and Random Forest algorithms produces 90.33% accuracy with the original labels, and 99.99% accuracy with the new labels for both training and testing dataset.

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11th IEEE International Congress on Conferences on Internet of Things, 14th IEEE International Conference on Green Computing and Communications, 11th IEEE International Conference on Cyber, Physical and Social Computing, 4th IEEE International Conference on Smart Data, 1st IEEE International Conference on Blockchain and 18th IEEE International Conference on Computer and Information Technology, iThings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018
Department of Systems and Computer Engineering

Patel, B. (Bhumika), Somani, Z. (Zaheenabanu), Ajila, S, & Lung, C.H. (2018). Hybrid Relabeled Model for Network Intrusion Detection. In Proceedings - IEEE 2018 International Congress on Cybermatics: 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, iThings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018 (pp. 872–877). doi:10.1109/Cybermatics_2018.2018.00167