A multiclass cascade of artificial neural network for network intrusion detection

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 pr...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2017-01, Vol.32 (4), p.2875-2883
Main Authors: Baig, Mirza M., Awais, Mian M., El-Alfy, El-Sayed M.
Format: Article
Language:English
Subjects:
Artificial neural networks
Communications traffic
Computer networks
Cybersecurity
Intrusion detection systems
Machine learning
Neural networks
Partitions
Training
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Summary: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.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-169230