Open Access

ARTICLE

Network Intrusion Detection Model Using Fused Machine Learning Technique

Fahad Mazaed Alotaibi^*

Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia

* Corresponding Author: Fahad Mazaed Alotaibi. Email:

Computers, Materials & Continua 2023, 75(2), 2479-2490. https://doi.org/10.32604/cmc.2023.033792

Received 28 June 2022; Accepted 22 September 2022; Issue published 31 March 2023

Abstract

With the progress of advanced technology in the industrial revolution encompassing the Internet of Things (IoT) and cloud computing, cyberattacks have been increasing rapidly on a large scale. The rapid expansion of IoT and networks in many forms generates massive volumes of data, which are vulnerable to security risks. As a result, cyberattacks have become a prevalent and danger to society, including its infrastructures, economy, and citizens’ privacy, and pose a national security risk worldwide. Therefore, cyber security has become an increasingly important issue across all levels and sectors. Continuous progress is being made in developing more sophisticated and efficient intrusion detection and defensive methods. As the scale of complexity of the cyber-universe is increasing, advanced machine learning methods are the most appropriate solutions for predicting cyber threats. In this study, a fused machine learning-based intelligent model is proposed to detect intrusion in the early stage and thus secure networks from harmful attacks. Simulation results confirm the effectiveness of the proposed intrusion detection model, with 0.909 accuracy and a miss rate of 0.091.

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Keywords

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