Authors :
Muthukrishnan Muthusubramanian; Ikram Ahamed Mohamed; Naveen Pakalapati
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/522dwfc4
Scribd :
https://tinyurl.com/5tfvjdru
DOI :
https://doi.org/10.5281/zenodo.10776751
Abstract :
Machine learning has emerged as a powerful
tool in the realm of cybersecurity, specifically in the
domain of threat detection and prevention. This abstract
delves into the pivotal role of machine learning algorithms
in fortifying cybersecurity measures to combat evolving
cyber threats. The integration of machine learning
techniques such as deep learning, support vector machines,
Bayesian classification, reinforcement learning, anomaly
detection, static file analysis, and behavioral analysis has
revolutionized the landscape of cybersecurity. These
algorithms enable organizations to automate threat
detection processes, enhance anomaly identification, and
bolster security defenses against sophisticated cyber-
attacks. By leveraging machine learning models,
cybersecurity professionals can swiftly analyze vast
amounts of data, detect malicious activities in real-time,
and proactively respond to potential threats. The efficacy
of machine learning in cybersecurity is evident through its
ability to augment analyst efficiency, provide expert
intelligence at scale, and automate manual tasks to
improve overall security posture.
Keywords :
Machine Learning, Cybersecurity, Threat Detection, Prevention, Deep Learning, Static File Analysis, Behavioral Analysis, Security Measures, Cyber Threats.
Machine learning has emerged as a powerful
tool in the realm of cybersecurity, specifically in the
domain of threat detection and prevention. This abstract
delves into the pivotal role of machine learning algorithms
in fortifying cybersecurity measures to combat evolving
cyber threats. The integration of machine learning
techniques such as deep learning, support vector machines,
Bayesian classification, reinforcement learning, anomaly
detection, static file analysis, and behavioral analysis has
revolutionized the landscape of cybersecurity. These
algorithms enable organizations to automate threat
detection processes, enhance anomaly identification, and
bolster security defenses against sophisticated cyber-
attacks. By leveraging machine learning models,
cybersecurity professionals can swiftly analyze vast
amounts of data, detect malicious activities in real-time,
and proactively respond to potential threats. The efficacy
of machine learning in cybersecurity is evident through its
ability to augment analyst efficiency, provide expert
intelligence at scale, and automate manual tasks to
improve overall security posture.
Keywords :
Machine Learning, Cybersecurity, Threat Detection, Prevention, Deep Learning, Static File Analysis, Behavioral Analysis, Security Measures, Cyber Threats.
