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International Conference on Information Security and Cryptology

Inscrypt 2017: Information Security and Cryptology pp 383–403Cite as

BotTokenizer: Exploring Network Tokens of HTTP-Based Botnet Using Malicious Network Traces

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10726))

Abstract

Nowadays, malicious software and especially botnets leverage HTTP protocol as their communication and command (C&C) channels to connect to the attackers and control compromised clients. Due to its large popularity and facility across firewall, the malicious traffic can blend with legitimate traffic and remains undetected. While network signature-based detection systems and models show extraordinary advantages, such as high detection efficiency and accuracy, their scalability and automatization still need to be improved.

In this work, we present BotTokenizer, a novel network signature-based detection system that aims to detect malicious HTTP C&C traffic. BotTokenizer automatically learns recognizable network tokens from known HTTP C&C communications from different botnet families by using words segmentation technologies. In essence, BotTokenizer implements a coarse-grained network signature generation prototype only relying on Uniform Resource Locators (URLs) in HTTP requests. Our evaluation results demonstrate that BotTokenizer performs very well on identifying HTTP-based botnets with an acceptable classification errors.

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Notes

  1. 1.

    Snort. http://www.snort.org/.

  2. 2.

    Suricata. http://suricata-ids.org/.

  3. 3.

    ANUBIS. http://www.assiste.com/Anubis.html/.

  4. 4.

    CWSandbox. http://www.cwsandbox.org/.

  5. 5.

    Alexa. http://www.alexa.com/topsites/.

  6. 6.

    MCFP. https://stratosphereips.org/category/dataset.html.

  7. 7.

    VirusTotal. https://www.virustotal.com/.

  8. 8.

    scikit-learn: http://scikit-learn.org/stable/.

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Author information

Authors and Affiliations

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

    Biao Qi, Zhixin Shi, Yan Wang, Jizhi Wang, Qiwen Wang & Jianguo Jiang

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

    Biao Qi, Yan Wang, Jizhi Wang & Qiwen Wang

Authors
  1. Biao Qi
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  2. Zhixin Shi
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  3. Yan Wang
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  4. Jizhi Wang
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  5. Qiwen Wang
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  6. Jianguo Jiang
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Corresponding author

Correspondence to Zhixin Shi .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Dongdai Lin

  3. Columbia University, New York, New York, USA

    Moti Yung

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Qi, B., Shi, Z., Wang, Y., Wang, J., Wang, Q., Jiang, J. (2018). BotTokenizer: Exploring Network Tokens of HTTP-Based Botnet Using Malicious Network Traces. In: Chen, X., Lin, D., Yung, M. (eds) Information Security and Cryptology. Inscrypt 2017. Lecture Notes in Computer Science(), vol 10726. Springer, Cham. https://doi.org/10.1007/978-3-319-75160-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-75160-3_23

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  • Online ISBN: 978-3-319-75160-3

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