A Multi-objective Genetic Algorithm Based Approach for Effective Intrusion Detection Using Neural Networks

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In this paper, a novel multi-objective genetic algorithm (MOGA) based approach is proposed for effective intrusion detection based on benchmark datasets. The proposed approach can generate a pool of non-inferior individual solutions and ensemble solutions thereof. The generated ensembles can be used to detect the intrusions accurately. For intrusion detection problem, the proposed MOGA based approach could consider conflicting objectives simultaneously like detection rate of each attack class, error rate, accuracy, diversity etc. The proposed approach can generate a pool of non-inferior solutions and their ensemble thereof having optimized trade-offs values of multiple conflicting objectives. In this paper, a three phase MOGA based approach is proposed to generate solutions with a simple chromosome design in first phase. In first phase, a Pareto front of non-inferior individual solutions is approximated. In the second phase of the proposed approach, entire solution set is further refined to determine effective ensemble solutions considering solution interaction. In this phase, another improved Pareto front of ensemble solutions over that of individual solutions is approximated. The ensemble solutions in improved Pareto front reported improved detection results based on benchmark datasets for intrusion detection. In third phase, a combination method like majority voting method is used to fuse the predictions of individual solutions for determining prediction of ensemble solution. Benchmark datasets namely KDD cup 1999 and ISCX 2012 dataset are used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover individual solutions and ensemble solutions thereof with good support and detection rate from benchmark datasets (in comparison with well-known ensemble methods like bagging and boosting). In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives and a dataset can be represented in the form of labeled instances in terms of its features.