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:: Volume 8, Number 3 (autumn 2017) ::
3 2017, 8(3): 191-200 Back to browse issues page
A New Light Weight Intrusion Detection Algorithm for Computer Networks
Mr Mehdi Najafi1, Dr Reza Rafeh *
1- Department of Computer engineering , Arak University, Arak, Iran
Department of Computer engineering , Arak University, Arak, Iran , reza.rafeh@gmail.com
Abstract:   (164 Views)

Feature selection is one of the key challenges in developing intrusion detection systems. Classification algorithms in intrusion detection systems may be inconvenient for problems having so many features, because the size of the search space grows exponentially in terms of the number of features. This is while most of the features may be either irrelevant or redundant. Therefore, considering only relevant features (i.e. feature selection) may have a significant impact on the performance of the classification algorithms. The Imperialist Competitive Algorithm (ICA) can be used as a feature selection method with a high convergence, but it sometimes gets trapped in a local optimum. On the contrary, the Genetic Algorithm (GA) is powerful enough in terms of search for solutions, but it suffers from late convergence. Therefore, using a combination of both algorithms for feature selection may result in a rapid convergence as well as in a high precision. In this paper, by applying the Assimilate operator of the ICA to the GA, we propose a new feature selection algorithm for intrusion detection systems. The proposed algorithm has been tested on the KDD99 dataset using the decision tree classification. The experimental results show that the proposed algorithm has improved the detection rate (95.03%), false alarm rate (1.46) and the speed of convergence (3.82 second).

Keywords: Feature Selection, Anomaly Detection, Genetic Algorithm, Intrusion Detection System
Full-Text [PDF 1100 kb]   (88 Downloads)    
Type of Study: Research | Subject: computer
Received: 2015/04/17 | Accepted: 2016/06/3 | Published: 2017/07/10
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Najafi M, Rafeh R. A New Light Weight Intrusion Detection Algorithm for Computer Networks. 3. 2017; 8 (3) :191-200
URL: http://adst.ir/article-1-750-en.html
Volume 8, Number 3 (autumn 2017) Back to browse issues page
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