Naveed, Muhammad ORCID: https://orcid.org/0000-0002-0923-4976, Arif, M., Usman, S.M., Anwar, Aamir and Umar, F. (2022) A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks. Wireless Communications and Mobile Computing.

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Abstract

An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violatingnetwork policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on datapreprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. Forthe purpose of anomaly identification, researchers have developed several machine learning and deep learning-basedalgorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid featureselection approach and a deep neural network- (DNN-) based classifier, the authors of this research suggest an enhancedintrusion detection system (IDS). In order to construct a subset of reduced and optimal features that may be used forclassification, a hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principalcomponent analysis (PCA), is applied. These methods are referred to as “the big three.” On the NSL-KDD dataset, thesuggested model receives training and is then evaluated. The proposed method was successful in achieving the followingresults: a reduction of input data by 40%, an average accuracy of 99.73%, a precision score of 99.75%, an F1 score of 99.72%,and an average training and testing time of 138% and 2.7 seconds, respectively. The findings of the experiments demonstratethat the proposed model is superior to the performance of the other comparison approaches

Item Type:	Article
Identifier:	10.1155/2022/2215852
Subjects:	Computing
Depositing User:	Marc Forster
Date Deposited:	11 Nov 2024 09:39
Last Modified:	11 Nov 2024 09:45
URI:	https://repository.uwl.ac.uk/id/eprint/12875

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