Open Access

ARTICLE

An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

Adil Hussain¹, Kashif Naseer Qureshi^2,*, Khalid Javeed³, Musaed Alhussein⁴

1 Department of Transportation Engineering, Chang’an University, Xi’an, China
2 Department of Electronic & Computer Engineering, University of Limerick, Limerick, V94 T9PX, Ireland
3 Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
4 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Kingdom of Saudi Arabia

* Corresponding Author: Kashif Naseer Qureshi. Email:

Computer Systems Science and Engineering 2023, 47(2), 2513-2528. https://doi.org/10.32604/csse.2023.040305

Received 13 March 2023; Accepted 23 May 2023; Issue published 28 July 2023

Abstract

Information and communication technologies are spreading rapidly due to their fast proliferation in many fields. The number of Internet users has led to a spike in cyber-attack incidents. E-commerce applications, such as online banking, marketing, trading, and other online businesses, play an integral role in our lives. Network Intrusion Detection System (NIDS) is essential to protect the network from unauthorized access and against other cyber-attacks. The existing NIDS systems are based on the Backward Oracle Matching (BOM) algorithm, which minimizes the false alarm rate and causes of high packet drop ratio. This paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and limitations. To address the existing system issues, this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network performance. The proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD datasets. The experimental results indicatedthat the proposed solution performed better than the existing solutions and achieved a 5.17% detection rate and a 0.22% lower false alarm rate than the existing solution.

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Keywords

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