A Novel Machine Learning Approach for Malware Detection

5 Pages Posted: 29 May 2019 Last revised: 10 Oct 2019

See all articles by Tarun Kumar

Tarun Kumar

Uttaranchal University

Sanjeev Sharma

Uttaranchal University

Himanshu Goel

Uttaranchal University

Sumit Chaudhary

Uttaranchal University

Date Written: March 14, 2019

Abstract

Malware means malicious software. Detecting malware over a system is malware analysis. It consists of two parts static analysis and dynamic analysis. Static analysis includes analysing a suspicious file and dynamic analysis means observing a file during its process time. In this paper, we have proposed a framework for malware analysis based on semi automated malware detection usually machine learning which is based on dynamic malware detection. The framework shows the quality of experience (QoE) to maintain the efficiency tradeoffs and uses the method of classification. The samples of malware also shows that the framework create a strong detection method.

Keywords: Malware, Disassembler, Evasion Attacks, Machine Learning

Suggested Citation

Kumar, Tarun and Sharma, Sanjeev and Goel, Himanshu and Chaudhary, Sumit, A Novel Machine Learning Approach for Malware Detection (March 14, 2019). International Conference on Advances in Engineering Science Management & Technology (ICAESMT) - 2019, Uttaranchal University, Dehradun, India, Available at SSRN: https://ssrn.com/abstract=3383953 or http://dx.doi.org/10.2139/ssrn.3383953

Tarun Kumar (Contact Author)

Uttaranchal University ( email )

Arcadia Grant
Premnagar
Dehradun, 248007
India

Sanjeev Sharma

Uttaranchal University ( email )

Arcadia Grant
Premnagar
Dehradun, 248007
India

Himanshu Goel

Uttaranchal University ( email )

Arcadia Grant
Premnagar
Dehradun, 248007
India

Sumit Chaudhary

Uttaranchal University ( email )

Arcadia Grant
Premnagar
Dehradun, 248007
India

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