A Malware and Variant Detection Method Using Function Call Graph Isomorphism

The huge influx of malware variants are generated using packing and obfuscating techniques. Current antivirus software use byte signature to identify known malware, and this method is easy to be deceived and generally ineffective for identifying malware variants. Antivirus experts use hash signature...

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Bibliographic Details
Main Authors: Jinrong Bai, Qibin Shi, Shiguang Mu
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Security and Communication Networks
Online Access:http://dx.doi.org/10.1155/2019/1043794
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spelling doaj-db1a4b26a9fa408b930f84ce1a9804d32020-11-24T21:58:58ZengHindawi-WileySecurity and Communication Networks1939-01141939-01222019-01-01201910.1155/2019/10437941043794A Malware and Variant Detection Method Using Function Call Graph IsomorphismJinrong Bai0Qibin Shi1Shiguang Mu2School of Mathematics and Information Technology, Yuxi Normal University, Yuxi 653100, ChinaYuxi E-government Network Management Center, Yuxi 653100, ChinaSchool of Physics and Electronic Engineering, Yuxi Normal University, Yuxi 653100, ChinaThe huge influx of malware variants are generated using packing and obfuscating techniques. Current antivirus software use byte signature to identify known malware, and this method is easy to be deceived and generally ineffective for identifying malware variants. Antivirus experts use hash signature to verify if captured sample is one of the malware databases, and this method cannot recognize malware variants whose hash signatures have changed completely. Function call graph is a high-level abstraction representation of a program and more stable and resilient than byte or hash signature. In this paper, function call graph is used as signature of a program, and two kinds of graph isomorphism algorithms are employed to identify known malware and its variants. Four experiments are designed to evaluate the performance of the proposed method. Experimental results indicate that the proposed method is effective and efficient for identifying known malware and a portion of their variants. The proposed method can also be used to index and locate a large-scale malware database and group malware to the corresponding family.http://dx.doi.org/10.1155/2019/1043794
collection DOAJ
language English
format Article
sources DOAJ
author Jinrong Bai
Qibin Shi
Shiguang Mu
spellingShingle Jinrong Bai
Qibin Shi
Shiguang Mu
A Malware and Variant Detection Method Using Function Call Graph Isomorphism
Security and Communication Networks
author_facet Jinrong Bai
Qibin Shi
Shiguang Mu
author_sort Jinrong Bai
title A Malware and Variant Detection Method Using Function Call Graph Isomorphism
title_short A Malware and Variant Detection Method Using Function Call Graph Isomorphism
title_full A Malware and Variant Detection Method Using Function Call Graph Isomorphism
title_fullStr A Malware and Variant Detection Method Using Function Call Graph Isomorphism
title_full_unstemmed A Malware and Variant Detection Method Using Function Call Graph Isomorphism
title_sort malware and variant detection method using function call graph isomorphism
publisher Hindawi-Wiley
series Security and Communication Networks
issn 1939-0114
1939-0122
publishDate 2019-01-01
description The huge influx of malware variants are generated using packing and obfuscating techniques. Current antivirus software use byte signature to identify known malware, and this method is easy to be deceived and generally ineffective for identifying malware variants. Antivirus experts use hash signature to verify if captured sample is one of the malware databases, and this method cannot recognize malware variants whose hash signatures have changed completely. Function call graph is a high-level abstraction representation of a program and more stable and resilient than byte or hash signature. In this paper, function call graph is used as signature of a program, and two kinds of graph isomorphism algorithms are employed to identify known malware and its variants. Four experiments are designed to evaluate the performance of the proposed method. Experimental results indicate that the proposed method is effective and efficient for identifying known malware and a portion of their variants. The proposed method can also be used to index and locate a large-scale malware database and group malware to the corresponding family.
url http://dx.doi.org/10.1155/2019/1043794
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