Review on machine and deep learning applications for cyber security

M. Thangavel, T. G.R. Abiramie Shree, P. Priyadharshini, T. Saranya

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

In today's world, everyone is generating a large amount of data on their own. With this amount of data generation, there is a change of security compromise of our data. This leads us to extend the security needs beyond the traditional approach which emerges the field of cyber security. Cyber security's core functionality is to protect all types of information, which includes hardware and software from cyber threats. The number of threats and attacks is increasing each year with a high difference between them. Machine learning and deep learning applications can be done to this attack, reducing the complexity to solve the problem and helping us to recover very easily. The algorithms used by both approaches are support vector machine (SVM), Bayesian algorithm, deep belief network (DBN), and deep random neural network (Deep RNN). These techniques provide better results than that of the traditional approach. The companies which use this approach in the real time scenarios are also covered in this chapter.

Original languageEnglish
Title of host publicationResearch Anthology on Machine Learning Techniques, Methods, and Applications
PublisherIGI Global
Pages1143-1164
Number of pages22
ISBN (Electronic)9781668462928
ISBN (Print)1668462915, 9781668462911
DOIs
Publication statusPublished - May 13 2022
Externally publishedYes

ASJC Scopus subject areas

  • General Computer Science

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