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A new machine learning method consisting of GA-LR and ANN for attack detection

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Abstract

Advances in computer networks led to the generation of much data that computer networks must be capable of transmitting. The security of this volume of data is a major challenge for companies. Intrusion detection systems is one of the solutions that researchers introduced for this challenge. This research aims to introduce a new machine learning model for intrusion detection. The proposed model includes two stages of feature selection and attack identification. The feature selection stage uses genetic algorithm and logistic regression algorithm to find a correlated subset of features. In the attack detection phase, the ANN algorithm is used. ANN is trained by particle optimization (PSO) and gravitational search (GS) algorithms. To evaluate the proposed model, two sets of NSL-KDD and KDD Cup'99 are used and results are compared with ANN based on gradient descent (GD-ANN) and decision tree, ANN based on genetic algorithm (GA-ANN) methods, ANN based on GSPSO (GSPSO-ANN), ANN based on PSO (PSO-ANN) and ANN based on GS (GS-ANN) indicate the superiority of the proposed method.

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Acknowledgements

The author would like to thank the WINE Editorial Board and the anonymous reviewers for their very helpful suggestions. Also, the author would like to extend their appreciation to Mr. Saman Rafiee Sardo and Mr. Behnam Mohammad Hassanizade for proof reading the manuscript and providing valuable comments.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

    Soodeh Hosseini

  2. Mahani Mathematical Research Center, Shahid Bahonar University of Kerman, Kerman, Iran

    Soodeh Hosseini

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  1. Soodeh Hosseini
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Correspondence to Soodeh Hosseini.

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Hosseini, S. A new machine learning method consisting of GA-LR and ANN for attack detection. Wireless Netw 26, 4149–4162 (2020). https://doi.org/10.1007/s11276-020-02321-3

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  • DOI: https://doi.org/10.1007/s11276-020-02321-3

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