Machine Learning for Cybersecurity: Threat Detection, Prevention, and Response
DOI:
https://doi.org/10.36676/dira.v12.i1.01Keywords:
cybersecurity, Malware classification, Intrusion detection, Botnet detection, Machine learning, Deep learningAbstract
Given the rapid evolution of threats in terms of both complexity and scope, cybersecurity has become an issue of the utmost importance in the digital age. When it comes to combating the ever-expanding environment of cyberattacks, traditional methods of threat detection and prevention are frequently ineffective. The purpose of this is to investigate the use of machine learning techniques to improve cybersecurity measures, with a particular emphasis on threat detection, prevention, and response. To begin, an examination of the principles of machine learning and the importance of this field to cybersecurity is presented. When it comes to recognising and mitigating cyber threats, a number of different machine learning methodologies, including deep learning, signature-based detection, and anomaly detection, are evaluated in terms of how effective they are.
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Copyright (c) 2024 Vikalp Thapliyal, Pranita Thapliyal
This work is licensed under a Creative Commons Attribution 4.0 International License.
