Abstract
Cyber threats are diversified in both volume and variety as most of the organizations develop and accept emerging technologies related to big data, cloud computing, and Internet of Things. In the area of cyber security, intrusion detection system (IDS) plays a significant role to identify the existing attack from network traffic. Over the past few decades, a lot of research works have been performed in this area. Researchers have used DARPA, KDD98, KDD99, and NSL-KDD datasets as a benchmark for their experiments. However, in the current network scenario, these datasets do not intuitively reflect proper network traffic and modern low footprint attacks. In this regard, this paper proposes a novel intrusion detection technique, where principal component analysis (PCA) has been used for dimensionality reduction, and multilayer perceptron (MLP) has been applied for classification. All the experiments have been conducted over the current UNSW-NB15 dataset consisting of a total of 2540,044 records with 9 different types of modern low footprint attacks. The experimental results demonstrate that the proposed misuse-based technique achieved a higher detection rate and low false alarm rate in comparison with other existing methods in the literature.
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Chowdhury, R., Roy, A., Saha, B., Bandyopadhyay, S.K. (2023). An Intelligent Intrusion Detection System Using a Novel Combination of PCA and MLP. In: Gyei-Kark, P., Jana, D.K., Panja, P., Abd Wahab, M.H. (eds) Engineering Mathematics and Computing. Studies in Computational Intelligence, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-19-2300-5_7
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