Affiliations: [a] School of Science and Engineering (SSE), Lahore University of Management Sciences (LUMS), Lahore, Pakistan
| [b] Department of Information and Computer Science, College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Correspondence:
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Corresponding author. Mian M. Awais, School of Science and Engineering (SSE), Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan. E-mail: [email protected].
Abstract: This paper presents a cascade of ensemble-based artificial neural network for multi-class intrusion detection (CANID) in computer network traffic. The proposed system learns a number of neural-networks connected as a cascade with each network trained using a small sample of training examples. The proposed cascade structure uses the trained neural network as a filter to partition the training data and hence a relatively small sample of training examples are used along with a boosting-based learning algorithm to learn an optimal set of neural network parameters for each successive partition. The performance of the proposed approach is evaluated and compared on the standard KDD CUP 1999 dataset as well as a very recent dataset, UNSW-NB15, composed of contemporary synthesized attack activities. Experimental results show that our proposed approach can efficiently detect various types of cyber attacks in computer networks.
