Open Access

ARTICLE

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

K. S. Bhuvaneshwari¹, K. Venkatachalam², S. Hubálovský^3,*, P. Trojovský⁴, P. Prabu⁵

1 Department of Computer Sceince and Engineering, Karpagam College of Engineering, Coimbatore, 641032, India
2 Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India
3 Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Králové, 50003, Czech Republic
4 Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Králové, 50003, Czech Republic
5 Department of Computer Sceince, CHRIST(Deemed to be University), Bangalore, 560074, India

* Corresponding Author: S. Hubálovský. Email:

Computers, Materials & Continua 2022, 70(3), 5949-5965. https://doi.org/10.32604/cmc.2022.020769

Received 06 June 2021; Accepted 07 July 2021; Issue published 11 October 2021

Abstract

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows while detecting the intruders. In this paper, deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing. The proposed system includes three phases such as preprocessing, feature selection and classification. In the first phase, KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method. In second phase, feature selection is performed by using Information Gain based Dragonfly Optimizer (IGDFO). Finally, Deep clustering based Convolutional Neural Network (CCNN) classifier optimized with Particle Swarm Optimization (PSO) identifies intrusion attacks efficiently. The clustering loss and network loss can be reduced with the optimization algorithm. We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics. The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall, f-measure and false detection rate.

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