Evaluating the Impact of Denial-of-Service (DoS) Attacks on Enterprise Networks Using Optimized Network Engineering Tools (OPNET 14.5) and Machine Learning

Abstract

The research conducts a network performance analysis of enterprise systems under Denial-of-Service (DoS) attacks through machine learning modeling with OPNET 14.5. Service interruptions along with financial losses result from Denial-of-Service attacks which seriously reduce network performance. The implementation of multiple defense measures has not resolved the persistent problem with real-time detection and response for enterprises. Through OPNET 14.5 simulation the research evaluates multiple DoS attack situations alongside their effects on performance metrics by measuring latency and achieving throughput and packet loss statistics. Two machine learning models with decision trees and support vector machines serve to detect normal and attack-related traffic patterns. The simulation demonstrates that networks experience severe degradation when under DoS attacks which leads to longer delays and packet drops. The machine learning detection systems show excellent attack pattern recognition abilities which indicates their practical use in preventing attacks. The authors suggest security frameworks should implement machine learning detection systems as part of their enterprise security infrastructure for better DoS protection. The research provides final proof about integrating network simulation along with machine learning technologies to stop DoS attacks which will enable further cybersecurity defense system development.