HANDOM: Heterogeneous Attention Network Model for Malicious Domain Detection

doi:10.1016/j.cose.2022.103059

	HANDOM: Heterogeneous Attention Network Model for Malicious Domain Detection
	Wang, Qing 1,2; Dong, Cong 3; Jian, Shijie 4; Du, Dan 1,2; Lu, Zhigang 1,2; Qi, Yinhao 1,2; Han, Dongxu 1,2; Ma, Xiaobo 5; Wang, Fei 6; Liu, Yuling 1,2
	2023-02-01
发表期刊	COMPUTERS & SECURITY
ISSN	0167-4048
卷号	125 页码:14
摘要	Malicious domains are crucial vectors for attackers to conduct malicious activities. With the increasing numbers in domain-based attack activities and the enhancement of attacker evasion methods, the de-tection of malicious domains has become critical and increasingly difficult. Statistical feature-based and graph structure-based detection methods are mainstream technical approaches. However, highly hidden domains can escape feature detection, and the detection range of graph structure-based methods is lim-ited. Based on these, we propose a malicious detection method called HANDOM. HANDOM combines statistical features and graph structural information to neutralize their limitations, and uses the Hetero-geneous Attention Network (HAN) model to jointly handle both information to achieve high-performance malicious domain classification. We conduct experimental evaluations on real-world datasets and com-pare HANDOM with machine learning methods and other malicious detection methods. The results present that HANDOM has superior and robust performance, and can identify highly hidden domains.(c) 2022 Elsevier Ltd. All rights reserved.
关键词	Malware domain detection Spatial -Temporal contextual correlation Heterogeneous attention network Statistical -and -Structural information
DOI	10.1016/j.cose.2022.103059
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021YFF0307203] ; National Key Research and Development Program of China[2019QY1303] ; National Key Research and Development Program of China[2019QY1302] ; NSFC[61902376] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDC02040100] ; National Engineering Research Center of Classified Protection and Safeguard Technology for Cybersecurity[C21640-3] ; NIM RD Project[35-AKYZD20 08-3] ; Program of Key Laboratory of Network Assessment Technology ; Chinese Academy of Sciences ; Program of Beijing Key Laboratory of Network Security and Protection Technology
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000911578800001
出版者	ELSEVIER ADVANCED TECHNOLOGY
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20082
专题	中国科学院计算技术研究所期刊论文
通讯作者	Liu, Yuling
作者单位	1.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China 3.Zhongguancun Lab, Beijing, Peoples R China 4.Minist Publ Secur, Res Inst 1, Beijing, Peoples R China 5.Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Qing,Dong, Cong,Jian, Shijie,et al. HANDOM: Heterogeneous Attention Network Model for Malicious Domain Detection[J]. COMPUTERS & SECURITY,2023,125:14.
APA	Wang, Qing.,Dong, Cong.,Jian, Shijie.,Du, Dan.,Lu, Zhigang.,...&Liu, Yuling.(2023).HANDOM: Heterogeneous Attention Network Model for Malicious Domain Detection.COMPUTERS & SECURITY,125,14.
MLA	Wang, Qing,et al."HANDOM: Heterogeneous Attention Network Model for Malicious Domain Detection".COMPUTERS & SECURITY 125(2023):14.