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HinPage: Illegal and Harmful Webpage Identification Using Transductive Classification

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Information Security and Cryptology (Inscrypt 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13837))

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Abstract

With the growing popularity of the Internet, websites could make significant profit by hosting illegal and harmful content, such as violence, sexual, illegal gambling, drug abuse, etc. They are serious threats to a safe and secure Internet, and they are especially harmful to the underage population. Government agencies, ISPs, network administrators at various levels, and parents have been seeking for accurate and robust solutions to block such illegal and harmful webpages. Existing solutions detect inappropriate pages based on content, e.g., using keyword matching or content-based image classification. They could be easily escaped by altering the internal format of texts or images, e.g., mixing different alphabets. In this paper, we propose to utilize relatively stable features extracted from the relationships between the targeted illegal/harmful webpages to discover and identify illegal webpages. We introduce a new mechanism, namely HinPage, that utilizes such features for the robust identification of PG (pornographic and gambling) pages. HinPage models the candidate PG pages and the resources on the pages with a heterogeneous information network (HIN). A transductive classification algorithm is then applied to the HIN to identify PG pages.

Through experiments on 10,033 candidate PG pages, we demonstrate that HinPage achieves an accuracy of 83.5% on PG page identification. In particular, it is able to identify illegal/harmful PG pages that cannot be recognized by SOTA commercial products.

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Acknowledgment

This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences with No. XDC02030000, the National Key Research and Development Program of China No. 2021YFB3101403 and National Key R &D Program 2021 (Grant No. 2021YFB3101001).

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Yunfan Li, Lingjing Yu & Qingyun Liu

  2. National Engineering Laboratory for Information Security Technologies, Beijing, China

    Yunfan Li, Lingjing Yu & Qingyun Liu

  3. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Yunfan Li

Authors
  1. Yunfan Li
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  2. Lingjing Yu
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  3. Qingyun Liu
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Corresponding author

Correspondence to Lingjing Yu .

Editor information

Editors and Affiliations

  1. Institute of Information Engineering, CAS, Beijing, China

    Yi Deng

  2. Columbia University, New York, NY, USA

    Moti Yung

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Li, Y., Yu, L., Liu, Q. (2023). HinPage: Illegal and Harmful Webpage Identification Using Transductive Classification. In: Deng, Y., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2022. Lecture Notes in Computer Science, vol 13837. Springer, Cham. https://doi.org/10.1007/978-3-031-26553-2_20

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  • DOI: https://doi.org/10.1007/978-3-031-26553-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26552-5

  • Online ISBN: 978-3-031-26553-2

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