Title: Cat deep system for intrusion detection in software defined networking

Authors: Yogita Hande; Akkalakshmi Muddana

Addresses: Gitam University, Rudraram, Telangana 502329, India ' Gitam University, Rudraram, Telangana 502329, India

Abstract: The development of software-defined networks (SDN) is the application of the network control, which is more convenient, secure and easy to develop and manage. This paper proposes an intrusion detection system (IDS) in SDN with the developed cat deep system (CDS). The training is done using the deep convolutional neural network (DCNN) with modified-cat swarm optimisation (M-CSO), which is the integration of the stochastic gradient descent (SGD) with the cat swarm optimisation (CSO). The sniffer, detector, and the sensor are the major components of the proposed system. All the packets are inspected with the sniffer to extract the features and these features are used to detect the abnormality using DCNN and then check the boundary to find the presence of attack in the system. The proposed CDS obtains the maximum accuracy, precision, recall, and F1_measure of 0.9203, 0.9498, 0.9277, and 0.9271, respectively, for NSL-KDD dataset.

Keywords: software-defined networks; cat deep system; cat swarm optimisation; duration; sniffer.

DOI: 10.1504/IJIIDS.2022.121841

International Journal of Intelligent Information and Database Systems, 2022 Vol.15 No.2, pp.125 - 165

Received: 19 Feb 2020
Accepted: 13 Jan 2021
Published online: 07 Apr 2022 *

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