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Review on Machine and Deep Learning Applications for Cyber Security

Review on Machine and Deep Learning Applications for Cyber Security
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Author(s): Thangavel M. (Thiagarajar College of Engineering, India), Abiramie Shree T. G. R. (Thiagarajar College of Engineering, India), Priyadharshini P. (Thiagarajar College of Engineering, India) and Saranya T. (Thiagarajar College of Engineering, India)
Copyright: 2020
Pages: 22
Source title: Handbook of Research on Machine and Deep Learning Applications for Cyber Security
Source Author(s)/Editor(s): Padmavathi Ganapathi (Avinashilingam Institute for Home Science and Higher Education for Women, India) and D. Shanmugapriya (Avinashilingam Institute for Home Science and Higher Education for Women, India)
DOI: 10.4018/978-1-5225-9611-0.ch003

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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.

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