Enhancing Intrusion Detection Systems for Secure E-Commerce Communication Networks
Nagagopiraju Vullam, D Roja, N.V.J. Rao, Sai Srinivas Vellela, Lakshma Reddy Vuyyuru, K Kiran Kumar
Abstract
Rapid advancements in information and communication technologies have permeated various sectors, leading to an exponential growth in Internet users. However, this surge in connectivity has also given rise to a corresponding increase in cyber-attacks. E-commerce applications, which have become integral to our daily lives, encompass online banking, marketing, trading, and numerous other online businesses. To safeguard these critical systems and networks, Network Intrusion Detection Systems (NIDS) play a pivotal role by thwarting unauthorized access and countering various cyber threats. Presently, the prevailing NIDS systems predominantly rely on the Backward Oracle Matching (BOM) algorithm. While effective in reducing false alarms, this algorithm has been associated with a high packet drop ratio. In light of these limitations, this paper examines the existing NIDS systems and the various pattern-matching techniques they employ, shedding light on their weaknesses and constraints. To overcome these shortcomings, the paper introduces an enhanced iteration of the BOM algorithm. This enhanced version leverages multiple pattern-matching methods, enhancing the overall performance of the NIDS system and subsequently fortifying network security. To validate the proposed solution, comprehensive simulations were conducted using established datasets such as Snort and NSL-KDD. The experimental results unequivocally demonstrate the superior performance of the proposed solution compared to existing approaches. Specifically, it achieved a remarkable 5.17% improvement in detection rate while concurrently reducing the false alarm rate by 0.22% compared to the existing solution. IN summary, this paper addresses critical issues inherent in current NIDS systems, providing a robust solution through an enhanced BOM algorithm that incorporates multiple pattern- Matching methods. The experimental findings underscore the effectiveness of this approach in bolstering network security, offering promising prospects for mitigating cyber threats in the realm of E-commerce applications and beyond.