A survey on mining program-graph features for malware analysis

MD Saiful Islam; MD Rafiqul Islam; A.S.M. Kayes; Chengfei Liu; Irfan Altas

doi:10.1007/978-3-319-23802-9_18

A survey on mining program-graph features for malware analysis

MD Saiful Islam, MD Rafiqul Islam, A.S.M. Kayes, Chengfei Liu, Irfan Altas

Swinburne University of Technology

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

Malware, which is a malevolent software, mostly programmed by attackers for either disrupting the normal computer operation or gaining access to private computer systems. A malware detector determines the malicious intent of a program and thereafter, stops executing the program if the program is malicious. While a substantial number of various malware detection techniques based on static and dynamic analysis has been studied for decades, malware detection based on mining program graph features has attracted recent attention. It is commonly believed that graph based representation of a program is a natural way to understand its semantics and thereby, unveil its execution intent. This paper presents a state of the art survey on mining program-graph features for malware detection. We have also outlined the challenges of malware detection based on mining program graph features for its successful deployment, and opportunities that can be explored in the future.

Original language	English
Title of host publication	International Conference on Security and Privacy in Communication Networks
Subtitle of host publication	10th International ICST Conference, SecureComm 2014 Beijing, China, September 24–26, 2014 Revised Selected Papers, Part II
Place of Publication	Belgium
Publisher	Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Pages	220-236
Number of pages	17
Volume	153
DOIs	https://doi.org/10.1007/978-3-319-23802-9_18
Publication status	Published - 2014
Event	International Conference on Security and Privacy in Communication Networks - Beijing, China, China Duration: 24 Sept 2014 → 26 Sept 2014 https://securecomm.eai-conferences.org/2014/index.html

Workshop

Workshop	International Conference on Security and Privacy in Communication Networks
Country/Territory	China
Period	24/09/14 → 26/09/14
Internet address	https://securecomm.eai-conferences.org/2014/index.html

Access to Document

10.1007/978-3-319-23802-9_18

Cite this

Islam, MD. S., Islam, MD. R., Kayes, A. S. M., Liu, C., & Altas, I. (2014). A survey on mining program-graph features for malware analysis. In International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014 Beijing, China, September 24–26, 2014 Revised Selected Papers, Part II (Vol. 153, pp. 220-236). Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. https://doi.org/10.1007/978-3-319-23802-9_18

Islam, MD Saiful ; Islam, MD Rafiqul ; Kayes, A.S.M. et al. / A survey on mining program-graph features for malware analysis. International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014 Beijing, China, September 24–26, 2014 Revised Selected Papers, Part II. Vol. 153 Belgium : Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2014. pp. 220-236

@inproceedings{607f4f5338f04918ae47e866c3c57660,

title = "A survey on mining program-graph features for malware analysis",

abstract = "Malware, which is a malevolent software, mostly programmed by attackers for either disrupting the normal computer operation or gaining access to private computer systems. A malware detector determines the malicious intent of a program and thereafter, stops executing the program if the program is malicious. While a substantial number of various malware detection techniques based on static and dynamic analysis has been studied for decades, malware detection based on mining program graph features has attracted recent attention. It is commonly believed that graph based representation of a program is a natural way to understand its semantics and thereby, unveil its execution intent. This paper presents a state of the art survey on mining program-graph features for malware detection. We have also outlined the challenges of malware detection based on mining program graph features for its successful deployment, and opportunities that can be explored in the future.",

keywords = "Graph features; Malware detection; Program graph",

author = "Islam, {MD Saiful} and Islam, {MD Rafiqul} and A.S.M. Kayes and Chengfei Liu and Irfan Altas",

year = "2014",

doi = "10.1007/978-3-319-23802-9_18",

language = "English",

volume = "153",

pages = "220--236",

booktitle = "International Conference on Security and Privacy in Communication Networks",

publisher = "Institute for Computer Sciences, Social Informatics and Telecommunications Engineering",

note = "International Conference on Security and Privacy in Communication Networks ; Conference date: 24-09-2014 Through 26-09-2014",

url = "https://securecomm.eai-conferences.org/2014/index.html",

}

Islam, MDS, Islam, MDR, Kayes, ASM, Liu, C & Altas, I 2014, A survey on mining program-graph features for malware analysis. in International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014 Beijing, China, September 24–26, 2014 Revised Selected Papers, Part II. vol. 153, Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Belgium, pp. 220-236, International Conference on Security and Privacy in Communication Networks , China, 24/09/14. https://doi.org/10.1007/978-3-319-23802-9_18

A survey on mining program-graph features for malware analysis. / Islam, MD Saiful; Islam, MD Rafiqul; Kayes, A.S.M. et al.
International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014 Beijing, China, September 24–26, 2014 Revised Selected Papers, Part II. Vol. 153 Belgium: Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2014. p. 220-236.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - A survey on mining program-graph features for malware analysis

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AU - Altas, Irfan

PY - 2014

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N2 - Malware, which is a malevolent software, mostly programmed by attackers for either disrupting the normal computer operation or gaining access to private computer systems. A malware detector determines the malicious intent of a program and thereafter, stops executing the program if the program is malicious. While a substantial number of various malware detection techniques based on static and dynamic analysis has been studied for decades, malware detection based on mining program graph features has attracted recent attention. It is commonly believed that graph based representation of a program is a natural way to understand its semantics and thereby, unveil its execution intent. This paper presents a state of the art survey on mining program-graph features for malware detection. We have also outlined the challenges of malware detection based on mining program graph features for its successful deployment, and opportunities that can be explored in the future.

AB - Malware, which is a malevolent software, mostly programmed by attackers for either disrupting the normal computer operation or gaining access to private computer systems. A malware detector determines the malicious intent of a program and thereafter, stops executing the program if the program is malicious. While a substantial number of various malware detection techniques based on static and dynamic analysis has been studied for decades, malware detection based on mining program graph features has attracted recent attention. It is commonly believed that graph based representation of a program is a natural way to understand its semantics and thereby, unveil its execution intent. This paper presents a state of the art survey on mining program-graph features for malware detection. We have also outlined the challenges of malware detection based on mining program graph features for its successful deployment, and opportunities that can be explored in the future.

KW - Graph features; Malware detection; Program graph

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DO - 10.1007/978-3-319-23802-9_18

M3 - Conference paper

VL - 153

SP - 220

EP - 236

BT - International Conference on Security and Privacy in Communication Networks

PB - Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

CY - Belgium

T2 - International Conference on Security and Privacy in Communication Networks

Y2 - 24 September 2014 through 26 September 2014

ER -

Islam MDS, Islam MDR, Kayes ASM, Liu C, Altas I. A survey on mining program-graph features for malware analysis. In International Conference on Security and Privacy in Communication Networks: 10th International ICST Conference, SecureComm 2014 Beijing, China, September 24–26, 2014 Revised Selected Papers, Part II. Vol. 153. Belgium: Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2014. p. 220-236 doi: 10.1007/978-3-319-23802-9_18

A survey on mining program-graph features for malware analysis

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