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

作　　者：Adil Hussain Kashif Naseer Qureshi Khalid Javeed Musaed Alhussein 

机构地区：[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

出　　处：《Computer Systems Science & Engineering》2023年第11期2513-2528,共16页计算机系统科学与工程（英文）

摘　　要：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 indicated that 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.

关 键 词：E-COMMERCE NIDS security algorithm network applications CIA detection 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]