Title: Intrusion detection and prevention with machine learning algorithms

Authors: Victor Chang; Sreeja Boddu; Qianwen Ariel Xu; Le Minh Thao Doan

Addresses: Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, England, UK ' Cybersecurity, Information Systems and AI Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, UK ' Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, England, UK ' Cybersecurity, Information Systems and AI Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, UK

Abstract: In recent decades, computer networks have played a key role in modern life and also have escalated the number of new attacks on internet traffics to avoid malicious activities. An Intrusion Detection System (IDS) is imperative for researching firewalls, anti-viruses and intrusion (bad connection). Many researchers are striving to overcome the challenges of IDS and focus on getting better accuracy to predict automatically normal data connection and abnormal data. To resolve the above problems, many researchers are focused on traditional machine learning and deep learning algorithms to detect automatically internal and external connections of network protocol. This paper adopts various Machine Learning (ML) techniques such as Bayes Network, Random Forest, Decision Table and Nearest Neighbour. The data set KDDcup-1999, which is the most reliable data set, contains a wide range of network environments. A framework for to catch attacks is also proposed with a detection rate of more than 98%. It suggested the potential application of this framework in practice to detect intrusion and contribute to the cybersecurity field.

Keywords: machine learning; deep learning; security data set; intrusion detection.

DOI: 10.1504/IJGUC.2023.135306

International Journal of Grid and Utility Computing, 2023 Vol.14 No.6, pp.617 - 631

Received: 01 Nov 2021
Accepted: 10 Dec 2021
Published online: 05 Dec 2023 *

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