TY - GEN
T1 - A Novel Malware Detection Framework Based on Weighted Heterograph
AU - Fan, Meihua
AU - Li, Shudong
AU - Han, Weihong
AU - Wu, Xiaobo
AU - Gu, Zhaoquan
AU - Tian, Zhihong
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/12/4
Y1 - 2020/12/4
N2 - Malware has always been one of the major threats to cyberspace security. Also, it has long been concerned by researchers of antivirus technology. However, malware gradually tends to be diversified and updated quickly, it is a big challenge to security personnel both in terms of number and attack means. In this paper, we propose a malware detection framework based on fine-grained malware behavior graph, which applies graph neural network to malware detection tasks. We design a fine-grained malicious behavior graph to represent the association of malware and its behavior associated entities. Then, aiming at learning the semantic information carried in the malicious behavior graph, we propose a weighted heterogeneous graph neural network named MalSage. We conducted a model evaluation of the proposed method on a malware dataset from VirusTotal. The results show that the proposed framework is more accurate than other algorithms in malware classification task.
AB - Malware has always been one of the major threats to cyberspace security. Also, it has long been concerned by researchers of antivirus technology. However, malware gradually tends to be diversified and updated quickly, it is a big challenge to security personnel both in terms of number and attack means. In this paper, we propose a malware detection framework based on fine-grained malware behavior graph, which applies graph neural network to malware detection tasks. We design a fine-grained malicious behavior graph to represent the association of malware and its behavior associated entities. Then, aiming at learning the semantic information carried in the malicious behavior graph, we propose a weighted heterogeneous graph neural network named MalSage. We conducted a model evaluation of the proposed method on a malware dataset from VirusTotal. The results show that the proposed framework is more accurate than other algorithms in malware classification task.
KW - Graph neural network
KW - Malware detection
KW - Weighted heterogeneous graph
UR - https://www.scopus.com/pages/publications/85098932135
U2 - 10.1145/3444370.3444545
DO - 10.1145/3444370.3444545
M3 - 会议稿件
AN - SCOPUS:85098932135
T3 - ACM International Conference Proceeding Series
SP - 39
EP - 43
BT - Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020
PB - Association for Computing Machinery
T2 - 2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020
Y2 - 4 December 2020 through 6 December 2020
ER -