Review of Contemporary Literature on Machine Learning based Malware Analysis and Detection Strategies

Authors

  • G. Bala Krishna

  • V. Radha

  • K. Venugopala Rao

Keywords:

malware detection, malware signature, API call sequence, anomalies, static analysis, dynamic analysis, machine learning

Abstract

Abstract: malicious software also known as malware are the critical security threat experienced by the current ear of internet and computer system users. The malwares can morph to access or control the system level operations in multiple dimensions. The traditional malware detection strategies detects by signatures, which are not capable to notify the unknown malwares. The machine learning models learns from the behavioral patterns of the existing malwares and attempts to notify the malwares with similar behavioral patterns, hence these strategies often succeeds to notify even about unknown malwares. This manuscript explored the detailed review of machine learning based malware detection strategies found in contemporary literature.

How to Cite

G. Bala Krishna, V. Radha, & K. Venugopala Rao. (2016). Review of Contemporary Literature on Machine Learning based Malware Analysis and Detection Strategies. Global Journal of Computer Science and Technology, 16(E5), 17–22. Retrieved from https://computerresearch.org/index.php/computer/article/view/1410

Review of Contemporary Literature on Machine Learning based Malware Analysis and Detection Strategies

Published

2016-03-15