A Survey on Cyber Security in Machine Learning
V. B. Pravalika · Zenodo (CERN European Organization for Nuclear Research) · 2020
Cybersecurity is proliferating everywhere, taking advantage of any form of network infrastructure weakness. More effort is paid by responsible hackers to analyse vulnerabilities and to propose methodologies for mitigation. An immediate demand has been for the production of successful techniques the cybersecurity community's sector. Machine learning for cybersecurity has recently become a subject of great interest because of its performance. Machine learning and deep learning in the area of cybersecurity. Machine learning approaches have been extended to significant cybersecurity problems. Issues such as identification of attack, recognition and identification of viruses, spam detection and identification of phishing. Though machine learning does not automate itself, a full cybersecurity infrastructure tends to more easily recognise cyber security risks than most software-oriented methodologies, thereby reducing cyber security challenges. The responsibility for safety analysts the ever changing existence of cyber threats continually encourages researchers to explore with the best a blend of strong cybersecurity and computer analysis skills. In this article, we discuss the latest state of the art frameworks for machine learning and their cybersecurity ability. It provides an overview of machine learning algorithms for the most prevalent forms of cybersecurity risks.