View in EDS

A SURVEY ON MACHINE LEARNING TECHNIQUES FOR DETECTION OF CYBERATTACK AND PERPETRATOR PREDICTION.

Saved in:
Bibliographic Details
Title: A SURVEY ON MACHINE LEARNING TECHNIQUES FOR DETECTION OF CYBERATTACK AND PERPETRATOR PREDICTION.
Authors: Shanmugapriya, M., Revathy, N. P., Suresh Kumar, V.
Source: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 4, p259-272, 14p
Subject Terms: COMPUTER networks, INTERNET security, MACHINE learning, WIRELESS Internet, SECURITY systems
Abstract: Cyberspace has grown as a result of the widespread usage of mobile apps and the Internet. Cyberspace is seeing an increase in long-term, automated cyberattacks. Cybersecurity strategies improve security systems to identify and thwart attacks. Because hackers are now skilled enough to circumvent conventional security measures, the security mechanisms that were previously in place are no longer adequate. Unknown and polymorphic security attacks are difficult for traditional security methods to identify. In many applications related to cyber security, machine learning (ML) techniques are essential. Even But there are still a lot of obstacles to overcome before ML systems can be considered reliable. Numerous malevolent adversaries with financial motives are prepared to take advantage of these ML vulnerabilities in cyberspace. By reviewing the literature on machine learning (ML) techniques for cyber security, including intrusion detection, spam detection, and malware detection on computer networks and mobile networks over the past ten years, this paper seeks to provide a thorough overview of the difficulties that ML techniques face in defending cyberspace against attacks. Along with regularly used security datasets, critical machine learning tools, assessment measures for assessing a classification model, and concise explanations of all machine learning techniques, it offers more. Lastly, the difficulties in implementing machine learning methods for cyber security are examined. The most recent comprehensive bibliography and the most recent developments in machine learning for cyber security are presented in this paper. [ABSTRACT FROM AUTHOR]
Copyright of Cuestiones de Fisioterapia is the property of Cuestiones de Fisioterapia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Biomedical Index
Description
Abstract:Cyberspace has grown as a result of the widespread usage of mobile apps and the Internet. Cyberspace is seeing an increase in long-term, automated cyberattacks. Cybersecurity strategies improve security systems to identify and thwart attacks. Because hackers are now skilled enough to circumvent conventional security measures, the security mechanisms that were previously in place are no longer adequate. Unknown and polymorphic security attacks are difficult for traditional security methods to identify. In many applications related to cyber security, machine learning (ML) techniques are essential. Even But there are still a lot of obstacles to overcome before ML systems can be considered reliable. Numerous malevolent adversaries with financial motives are prepared to take advantage of these ML vulnerabilities in cyberspace. By reviewing the literature on machine learning (ML) techniques for cyber security, including intrusion detection, spam detection, and malware detection on computer networks and mobile networks over the past ten years, this paper seeks to provide a thorough overview of the difficulties that ML techniques face in defending cyberspace against attacks. Along with regularly used security datasets, critical machine learning tools, assessment measures for assessing a classification model, and concise explanations of all machine learning techniques, it offers more. Lastly, the difficulties in implementing machine learning methods for cyber security are examined. The most recent comprehensive bibliography and the most recent developments in machine learning for cyber security are presented in this paper. [ABSTRACT FROM AUTHOR]
ISSN:11358599