Decoding Network Anomalies using Supervised Machine Learning and Deep Learning Approaches
Prem Naresh, Parasagani Srinath, Koduru Akshit, Manubothula Samba Shiva Raju, Pachava VenkataTeja
Abstract
In today’s interconnected world, where digital systems and networks underpin nearly every facet of modern life, cybersecurity has emerged as a paramount concern. The digital landscape is rife with threats, and the adversaries behind these threats continually evolve, growing more sophisticated and relentless. Intrusion Detection Systems (IDS) stand as the first line of defense, tirelessly monitoring and analyzing network traffic and system behavior to identify and respond to potential security breaches. However, the traditional rule-based IDS solutions, which have long served as the guardians of cyberspace, grapple with limitations in adapting to the ever-shifting tactics of cyber adversaries. In response to this challenge, this study presents a deep learning-based IDS, harnessing the capabilities of machine learning to significantly enhance the detection accuracy.