Open Access

ARTICLE

A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

Abdullah Alsaleh^1,2,*

1 Department of Information Engineering, Florence University, Florence, Italy
2 Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi Arabia

* Corresponding Author: Abdullah Alsaleh. Email:

(This article belongs to the Special Issue: Trustworthy Artificial Intelligence for Smart City)

Computer Systems Science and Engineering 2024, 48(2), 431-449. https://doi.org/10.32604/csse.2023.043107

Received 21 June 2023; Accepted 02 November 2023; Issue published 19 March 2024

Abstract

With the increasing number of connected devices in the Internet of Things (IoT) era, the number of intrusions is also increasing. An intrusion detection system (IDS) is a secondary intelligent system for monitoring, detecting and alerting against malicious activity. IDS is important in developing advanced security models. This study reviews the importance of various techniques, tools, and methods used in IoT detection and/or prevention systems. Specifically, it focuses on machine learning (ML) and deep learning (DL) techniques for IDS. This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles. To speed up the detection of recent attacks, the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network (CNN), which performs better than a support vector machine (SVM). Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy. The nearest class mean classifier is applied during the testing phase to identify new attacks. Experimental results using the AWID dataset, which is one of the most common open intrusion detection datasets, revealed a higher detection accuracy (94%) compared to SVM and random forest methods.

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Keywords

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