Visible to the public Radial Basis Function (RBF) Based on Multistage Autoencoders for Intrusion Detection system (IDS)

TitleRadial Basis Function (RBF) Based on Multistage Autoencoders for Intrusion Detection system (IDS)
Publication TypeConference Paper
Year of Publication2019
AuthorsKadhim, Y., Mishra, A.
Conference Name2019 1st International Informatics and Software Engineering Conference (UBMYK)
Date PublishedNov. 2019
PublisherIEEE
ISBN Number978-1-7281-3992-0
Keywordsauto-encoders, composability, feature extraction, feature selection, IDS, IDS attacks, Intrusion detection, intrusion detection system, learning (artificial intelligence), Mathematical model, MATLAB, MATLAB2018, pubcrawl, radial basis function, radial basis function networks, RBF, RBF training, RBF-based multistage autoencoders, resilience, Resiliency, security of data, Support vector machines, Testing, Training
Abstract

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to detect IDS attacks with 98.80% accuracy when validated using UNSW-NB15 dataset. The experimental results show the proposed method presents satisfactory results when compared with those obtained in this field.

URLhttps://ieeexplore.ieee.org/document/8965627/
DOI10.1109/UBMYK48245.2019.8965627
Citation Keykadhim_radial_2019