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A Novel Approach for Predicting the Malware Attacks

by Ekta Rokkathapa, Soumen Kanrar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 45
Year of Publication: 2019
Authors: Ekta Rokkathapa, Soumen Kanrar
10.5120/ijca2019918585

Ekta Rokkathapa, Soumen Kanrar . A Novel Approach for Predicting the Malware Attacks. International Journal of Computer Applications. 181, 45 ( Mar 2019), 30-32. DOI=10.5120/ijca2019918585

@article{ 10.5120/ijca2019918585,
author = { Ekta Rokkathapa, Soumen Kanrar },
title = { A Novel Approach for Predicting the Malware Attacks },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2019 },
volume = { 181 },
number = { 45 },
month = { Mar },
year = { 2019 },
issn = { 0975-8887 },
pages = { 30-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number45/30423-2019918585/ },
doi = { 10.5120/ijca2019918585 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:09:12.141769+05:30
%A Ekta Rokkathapa
%A Soumen Kanrar
%T A Novel Approach for Predicting the Malware Attacks
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 45
%P 30-32
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
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 analyzing 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.

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Index Terms

Computer Science
Information Sciences

Keywords

Malware attacks disassembler evasion attacks machine learning

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