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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

Valliammal Narayan, Barani Shaju
Copyright: © 2020 |Pages: 28
ISBN13: 9781522596110|ISBN10: 1522596119|EISBN13: 9781522596134
DOI: 10.4018/978-1-5225-9611-0.ch006
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MLA

Narayan, Valliammal, and Barani Shaju. "Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods." Handbook of Research on Machine and Deep Learning Applications for Cyber Security, edited by Padmavathi Ganapathi and D. Shanmugapriya, IGI Global, 2020, pp. 104-131. https://doi.org/10.4018/978-1-5225-9611-0.ch006

APA

Narayan, V. & Shaju, B. (2020). Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods. In P. Ganapathi & D. Shanmugapriya (Eds.), Handbook of Research on Machine and Deep Learning Applications for Cyber Security (pp. 104-131). IGI Global. https://doi.org/10.4018/978-1-5225-9611-0.ch006

Chicago

Narayan, Valliammal, and Barani Shaju. "Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods." In Handbook of Research on Machine and Deep Learning Applications for Cyber Security, edited by Padmavathi Ganapathi and D. Shanmugapriya, 104-131. Hershey, PA: IGI Global, 2020. https://doi.org/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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