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Article type: Research Article
Authors: Nawshin, Sabila | Islam, Salekul; * | Shatabda, Swakkhar
Affiliations: Departement of CSE, United International University, United City, Madani Avenue, Badda, Dhaka, Bangladesh
Correspondence: [*] Corresponding author. Salekul Islam, Departement of CSE, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh. E-mail: [email protected].
Abstract: Software Defined Networking (SDN) proposes a centralized network paradigm where a central controller manages the network. While this centralizes scheme opens up previously unachievable opportunities, it also makes the network more susceptible to a varying range of cyber threats. The development of effective Intrusion Detection Systems (IDS) designed for the SDN topology is a critical need to address the different vulnerabilities SDN faces. Towards that purpose, the inSDN dataset was specifically curated for intrusion detection in SDN with various attack scenarios unique to the SDN topology. This study leveraged the inSDN dataset to introduce an innovative Intrusion Detection System (IDS) model that amalgamates Principal Component Analysis (PCA), a dimensionality reduction technique widely employed in traditional Machine Learning (ML) to extract the principal features of the dataset and couples it with Artificial Neural Networks (ANN) to classify network traffic based on the extracted features. The proposed model attains an exceptional accuracy rate of 99.95% for multi-class classification and demonstrate that it surpasses the current state-of-the-art techniques while operating within a much simpler framework. This significantly diminishes the necessity for complex models that demand extensive computational resources when dealing with the inSDN attack dataset. The analysis of the dataset carried out in this study also provides insights into the redundancy present in the dataset and identifies the core features that contains most of the information in the dataset.
Keywords: Software Defined Networking (SDN), Intrusion Detection Systems (IDS), Principle Component Analysis (PCA), Artificial Neural Network (ANN)
DOI: 10.3233/JIFS-236340
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
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