Analyzing attack patterns targeting distributed network systems: A comprehensive classification and detection approach

Abstract

The 1990s saw the emergence of the Internet and the following growth of electronic commerce, or E-Commerce, which gave birth to dynamic new business settings like E-Business in today's fast changing Information Technology (IT) landscape. Due to this change, in-person interactions are no longer necessary for transactions to take place. An increasing number of electronic payment (E-Payment) systems are being developed as a result of the internet being the primary source of information for consumers when making selections about what to buy. Distributed wireless networks, which enable effective operations and communication across diverse organisations, have simultaneously become indispensable to our globally networked world. But this greater dependence on dispersed networks has also made the threat environment more diverse. The security, consistency, and accessibility of data and services inside these systems are seriously jeopardised by cybersecurity vulnerabilities and assaults. To properly manage these risks, it is essential to comprehend these weaknesses and create strong defences. Understanding and managing security risks is critical in this ever changing world. An extensive categorization and detection method for examining attack patterns directed at dispersed network systems is presented in this study. Based on their behaviour, goal, and effect, we rigorously classify attack patterns into discrete types, offering a comprehensive taxonomy that covers a broad spectrum of possible threats. Our categorization approach predicts and identifies new attack vectors by using cutting-edge methods from data analytics and machine learning. In order to identify anomalies from typical network behaviour and enable the early identification of possible security breaches, we provide a reliable detection approach that makes use of anomaly detection and real-time monitoring. This technique reduces false-positive rates and improves detection accuracy when integrated with current security infrastructures. The usefulness of our suggested techniques is confirmed by case studies and experimental assessments conducted in various network contexts. The findings highlight the value of a complete approach to threat analysis by showing how thorough categorization and detection may greatly increase dispersed network systems' resistance to sophisticated cyberattacks. To safeguard complex network infrastructures from constantly changing threats, cybersecurity experts may benefit from this research's insightful analysis and useful tools.