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Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods

Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods
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Author(s): Valliammal Narayan (Avinashilingam Institute for Home Science and Higher Education for Women, India) and Barani Shaju (Avinashilingam Institute for Home Science and Higher Education for Women, India)
Copyright: 2020
Pages: 28
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.ch006

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Abstract

This chapter aims to discuss applications of machine learning in cyber security and explore how machine learning algorithms help to fight cyber-attacks. Cyber-attacks are wide and varied in multiple forms. The key benefit of machine learning algorithms is that it can deep dive and analyze system behavior and identify anomalies which do not correlate with expected behavior. Algorithms can be trained to observe multiple data sets and strategize payload beforehand in detection of malware analysis.

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