Research Article

A Novel Approach for Predicting the Malware Attacks

by  Ekta Rokkathapa, Soumen Kanrar
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Issue 45
Published: Mar 2019
Authors: Ekta Rokkathapa, Soumen Kanrar
10.5120/ijca2019918585
PDF

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 },
                        year    = { 2019 },
                        volume  = { 181 },
                        number  = { 45 },
                        pages   = { 30-32 },
                        doi     = { 10.5120/ijca2019918585 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2019
                        %A Ekta Rokkathapa
                        %A Soumen Kanrar
                        %T A Novel Approach for Predicting the Malware Attacks%T 
                        %J International Journal of Computer Applications
                        %V 181
                        %N 45
                        %P 30-32
                        %R 10.5120/ijca2019918585
                        %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
No index terms available.
Keywords

Malware attacks disassembler evasion attacks machine learning

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