Decoding Network Anomalies using Supervised Machine Learning and Deep Learning Approaches

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 continual ly 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.