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V. Engen, “Machine Learning for Network Based Intrusion Detection: An Investigation into Discrepancies in Findings with the KDD Cup ‘99 Data Set and Multi-Objective Evolution of Neural Network Classifier Ensembles for Imbalanced Data,” Ph.D. Thesis, School of Design, Engineering and Computing, Bournemouth University, Bournemouth, 2010.

has been cited by the following article:

• TITLE: The Use of Multi-Objective Genetic Algorithm Based Approach to Create Ensemble of ANN for Intrusion Detection

  AUTHORS: Gulshan Kumar, Krishan Kumar

  KEYWORDS: Ensemble Classifiers; Intrusion Detection System; Intrusion Detection; Multi-Objective Genetic Algorithm

  JOURNAL NAME: International Journal of Intelligence Science, Vol.2 No.4A, November 1, 2012

  ABSTRACT: Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In literature, many researchers utilized Artificial Neural Networks (ANN) in supervised learning based intrusion detection successfully. Here, ANN maps the network traffic into predefined classes i.e. normal or specific attack type based upon training from label dataset. However, for ANN-based IDS, detection rate (DR) and false positive rate (FPR) are still needed to be improved. In this study, we propose an ensemble approach, called MANNE, for ANN-based IDS that evolves ANNs by Multi Objective Genetic algorithm to solve the problem. It helps IDS to achieve high DR, less FPR and in turn high intrusion detection capability. The procedure of MANNE is as follows: firstly, a Pareto front consisting of a set of non-dominated ANN solutions is created using MOGA, which formulates the base classifiers. Subsequently, based upon this pool of non-dominated ANN solutions as base classifiers, another Pareto front consisting of a set of non-dominated ensembles is created which exhibits classification tradeoffs. Finally, prediction aggregation is done to get final ensemble prediction from predictions of base classifiers. Experimental results on the KDD CUP 1999 dataset show that our proposed ensemble approach, MANNE, outperforms ANN trained by Back Propagation and its ensembles using bagging & boosting methods in terms of defined performance metrics. We also compared our approach with other well-known methods such as decision tree and its ensembles using bagging & boosting methods.