Universiti Teknologi Malaysia Institutional Repository

Malware detection based on hybrid signature behavior application programming interface call graph

Elhadi, Ammar Ahmed E. and Maarof, Mohd Aizaini and Osman, Ahmed Hamza (2012) Malware detection based on hybrid signature behavior application programming interface call graph. American Journal Of Applied Sciences, 9 (3). pp. 283-288. ISSN 1546-9239

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Official URL: http://dx.doi.org/10.3844/ajassp.2012.283.288

Abstract

Problem statement: A malware is a program that has malicious intent. Nowadays, malware authors apply several sophisticated techniques such as packing and obfuscation to avoid malware detection. That makes zero-day attacks and false positives the most challenging problems in the malware detection field. Approach: In this study, the static and dynamic analysis techniques that are used in malware detection are surveyed. Static analysis techniques, dynamic analysis techniques and their combination including Signature-Based and Behaviour-Based techniques are discussed. Results: In addition, a new malware detection framework is proposed. Conclusion: The proposed framework combines Signature-Based with Behaviour-Based using API graph system. The goal of the proposed framework is to improve accuracy and scan process time for malware detection.

Item Type:Article
Uncontrolled Keywords:Signature-Based, malware detection, dynamic analysis techniques
Subjects:Q Science
Divisions:Computer Science and Information System (Formerly known)
ID Code:47170
Deposited By: Narimah Nawil
Deposited On:22 Jun 2015 13:56
Last Modified:31 Mar 2019 16:34

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