Leverage temporal convolutional network for the representation learning of URLs

Yunji Liang; Jian Kang; Zhiwen Yu; Bin Guo; Xiaolong Zheng; Saike He

doi:10.1109/ISI.2019.8823362

Leverage temporal convolutional network for the representation learning of URLs

Yunji Liang
, Jian Kang
, Zhiwen Yu
, Bin Guo
, Xiaolong Zheng
, Saike He

计算机学院

Northwestern Polytechnical University Xian
CAS - Institute of Automation

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

7 引用（Scopus）

摘要

Cyber crimes including computer virus/malwares, spam, illegal sales, and phishing websites are proliferated aggressively via the disguised Uniform Resource Locators (URL). Although numerous studies were conducted for the URL classification task, the traditional URL classification solutions retreated due to the hand-crafted feature engineering and the boom of newly generated URLs. In this paper, we study the representation learning of URLs, and explore the URL classification using deep learning. Specifically, we propose URL2vec to extract both the structural and lexical features of URLs, and apply temporal convolutional network (TCN) for the URL classification task. The experimental results show that URL2vec outperforms both word2vec and character-level embedding for URL representation, and TCN achieves the best performance than baselines with the precision up to 95.97%.

源语言	英语
主期刊名	2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019
编辑	Xiaolong Zheng, Ahmed Abbasi, Michael Chau, Alan Wang, Lina Zhou
出版商	Institute of Electrical and Electronics Engineers Inc.
页	74-79
页数	6
ISBN（电子版）	9781728125046
DOI	https://doi.org/10.1109/ISI.2019.8823362
出版状态	已出版 - 7月 2019
活动	17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 - Shenzhen, 中国期限: 1 7月 2019 → 3 7月 2019

出版系列

姓名	2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019

会议

会议	17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019
国家/地区	中国
市	Shenzhen
时期	1/07/19 → 3/07/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 16 和平、正义和强大机构

访问文件

10.1109/ISI.2019.8823362

其它文件与链接

链接到 Scopus 的出版物

引用此

Liang, Y., Kang, J., Yu, Z., Guo, B., Zheng, X., & He, S. (2019). Leverage temporal convolutional network for the representation learning of URLs. 在 X. Zheng, A. Abbasi, M. Chau, A. Wang, & L. Zhou (编辑), 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019 (页码 74-79). 文章 8823362 (2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2019.8823362

Liang, Yunji ; Kang, Jian ; Yu, Zhiwen 等. / Leverage temporal convolutional network for the representation learning of URLs. 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019. 编辑 / Xiaolong Zheng ; Ahmed Abbasi ; Michael Chau ; Alan Wang ; Lina Zhou. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 74-79 (2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019).

@inproceedings{cf2842b7d3c342aaa26c48152393faa3,

title = "Leverage temporal convolutional network for the representation learning of URLs",

abstract = "Cyber crimes including computer virus/malwares, spam, illegal sales, and phishing websites are proliferated aggressively via the disguised Uniform Resource Locators (URL). Although numerous studies were conducted for the URL classification task, the traditional URL classification solutions retreated due to the hand-crafted feature engineering and the boom of newly generated URLs. In this paper, we study the representation learning of URLs, and explore the URL classification using deep learning. Specifically, we propose URL2vec to extract both the structural and lexical features of URLs, and apply temporal convolutional network (TCN) for the URL classification task. The experimental results show that URL2vec outperforms both word2vec and character-level embedding for URL representation, and TCN achieves the best performance than baselines with the precision up to 95.97\%.",

keywords = "Cyber Crime, Temporal Convolutional Network, URL Classification, URL2vec",

author = "Yunji Liang and Jian Kang and Zhiwen Yu and Bin Guo and Xiaolong Zheng and Saike He",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 ; Conference date: 01-07-2019 Through 03-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ISI.2019.8823362",

language = "英语",

series = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "74--79",

editor = "Xiaolong Zheng and Ahmed Abbasi and Michael Chau and Alan Wang and Lina Zhou",

booktitle = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",

}

Liang, Y, Kang, J, Yu, Z, Guo, B, Zheng, X & He, S 2019, Leverage temporal convolutional network for the representation learning of URLs. 在 X Zheng, A Abbasi, M Chau, A Wang & L Zhou (编辑), 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019., 8823362, 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019, Institute of Electrical and Electronics Engineers Inc., 页码 74-79, 17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019, Shenzhen, 中国, 1/07/19. https://doi.org/10.1109/ISI.2019.8823362

Leverage temporal convolutional network for the representation learning of URLs. / Liang, Yunji; Kang, Jian; Yu, Zhiwen 等.
2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019. 编辑 / Xiaolong Zheng; Ahmed Abbasi; Michael Chau; Alan Wang; Lina Zhou. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 74-79 8823362 (2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Leverage temporal convolutional network for the representation learning of URLs

AU - Liang, Yunji

AU - Kang, Jian

AU - Yu, Zhiwen

AU - Guo, Bin

AU - Zheng, Xiaolong

AU - He, Saike

PY - 2019/7

Y1 - 2019/7

N2 - Cyber crimes including computer virus/malwares, spam, illegal sales, and phishing websites are proliferated aggressively via the disguised Uniform Resource Locators (URL). Although numerous studies were conducted for the URL classification task, the traditional URL classification solutions retreated due to the hand-crafted feature engineering and the boom of newly generated URLs. In this paper, we study the representation learning of URLs, and explore the URL classification using deep learning. Specifically, we propose URL2vec to extract both the structural and lexical features of URLs, and apply temporal convolutional network (TCN) for the URL classification task. The experimental results show that URL2vec outperforms both word2vec and character-level embedding for URL representation, and TCN achieves the best performance than baselines with the precision up to 95.97%.

AB - Cyber crimes including computer virus/malwares, spam, illegal sales, and phishing websites are proliferated aggressively via the disguised Uniform Resource Locators (URL). Although numerous studies were conducted for the URL classification task, the traditional URL classification solutions retreated due to the hand-crafted feature engineering and the boom of newly generated URLs. In this paper, we study the representation learning of URLs, and explore the URL classification using deep learning. Specifically, we propose URL2vec to extract both the structural and lexical features of URLs, and apply temporal convolutional network (TCN) for the URL classification task. The experimental results show that URL2vec outperforms both word2vec and character-level embedding for URL representation, and TCN achieves the best performance than baselines with the precision up to 95.97%.

KW - Cyber Crime

KW - Temporal Convolutional Network

KW - URL Classification

KW - URL2vec

UR - https://www.scopus.com/pages/publications/85072973108

U2 - 10.1109/ISI.2019.8823362

DO - 10.1109/ISI.2019.8823362

M3 - 会议稿件

AN - SCOPUS:85072973108

T3 - 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019

SP - 74

EP - 79

BT - 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019

A2 - Zheng, Xiaolong

A2 - Abbasi, Ahmed

A2 - Chau, Michael

A2 - Wang, Alan

A2 - Zhou, Lina

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019

Y2 - 1 July 2019 through 3 July 2019

ER -

Liang Y, Kang J, Yu Z, Guo B, Zheng X, He S. Leverage temporal convolutional network for the representation learning of URLs. 在 Zheng X, Abbasi A, Chau M, Wang A, Zhou L, 编辑, 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 页码 74-79. 8823362. (2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019). doi: 10.1109/ISI.2019.8823362

Leverage temporal convolutional network for the representation learning of URLs

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此