Exploring methods to enhance network security through artificial intelligence

Abstract

In the dynamic landscape of cybersecurity, traditional rule-based systems find themselves frequently outstripped by the intricacy, variety, and mutable nature of cyber threats. This paper explores the capabilities of Machine Learning (ML) in detecting cyber-attacks, offering a fresh perspective to fortify cyber defense mechanisms. Through its unparalleled strengths in data analysis, pattern discernment, and outcome prediction, Machine Learning emerges as a promising ally in grappling with the multifaceted challenges posed by cyber adversaries. The exploration zeroes in on the potential of utilizing machine learning for cyber-attack detection, spotlighting supervised learning algorithms such as SVM and Random Forest. Experimental findings robustly underscore the value of Machine Learning in identifying potential cyber threats. In conclusion, the transformative potential of Machine Learning in the domain of cyber-attack detection is evident. Equipped with the prowess to derive insights from vast data sets, swiftly adapt to changing parameters, and preemptively recognize threats, Machine Learning promises to redefine the paradigms of cybersecurity. As the digital expanse continues to evolve, defense mechanisms must also evolve, with Machine Learning serving as a pivotal tool in this endeavor.