| Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning | |
Liang, Yunji1; Wang, Qiushi1; Xiong, Kang1; Zheng, Xiaolong2 ; Yu, Zhiwen1; Zeng, Daniel2
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| Source Publication | IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
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| ISSN | 1545-5971 |
| 2022-03-01 | |
| Volume | 19Issue:2Pages:717-730 |
| Corresponding Author | Liang, Yunji(liangyunji@nwpu.edu.cn) |
| Abstract | As 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. |
| Keyword | paced learning temporal convolutional network factorization machine wide & deep malicious URL detection |
| DOI | 10.1109/TDSC.2021.3121388 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and Development Program[2018AAA0100500] ; Natural Science Foundation of China[61902320] ; Fundamental Research Funds for the Central Universities[31020180QD140] |
| Funding Organization | National Key Research and Development Program ; Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Computer Science, Software Engineering |
| WOS ID | WOS:000767856000001 |
| Publisher | IEEE COMPUTER SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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 Author | Liang, Yunji |
| Affiliation | 1.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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