Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning
Liang, Yunji1; Wang, Qiushi1; Xiong, Kang1; Zheng, Xiaolong2; Yu, Zhiwen1; Zeng, Daniel2
Source PublicationIEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
ISSN1545-5971
2022-03-01
Volume19Issue:2Pages:717-730
Corresponding AuthorLiang, Yunji(liangyunji@nwpu.edu.cn)
AbstractAs cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. In spite of the diversity of cybercrimes, it is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they cannot provide robust performance due to the diversity of cybercrimes and cannot cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.
Keywordpaced learning temporal convolutional network factorization machine wide & deep malicious URL detection
DOI10.1109/TDSC.2021.3121388
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program[2018AAA0100500] ; Natural Science Foundation of China[61902320] ; Fundamental Research Funds for the Central Universities[31020180QD140]
Funding OrganizationNational Key Research and Development Program ; Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Information Systems ; Computer Science, Software Engineering
WOS IDWOS:000767856000001
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48079
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorLiang, Yunji
Affiliation1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710060, Shaanxi, Peoples R China
2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Liang, Yunji,Wang, Qiushi,Xiong, Kang,et al. Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning[J]. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING,2022,19(2):717-730.
APA Liang, Yunji,Wang, Qiushi,Xiong, Kang,Zheng, Xiaolong,Yu, Zhiwen,&Zeng, Daniel.(2022).Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning.IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING,19(2),717-730.
MLA Liang, Yunji,et al."Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning".IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 19.2(2022):717-730.
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