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Botnet Detection Based On Machine Learning Techniques Using DNS Query Data

Author

Listed:
  • Xuan Dau Hoang

    (Posts and Telecommunications Institute of Technology, Hanoi 100000, Vietnam)

  • Quynh Chi Nguyen

    (Samsung SVMC, Hanoi 100000, Vietnam)

Abstract

In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%.

Suggested Citation

  • Xuan Dau Hoang & Quynh Chi Nguyen, 2018. "Botnet Detection Based On Machine Learning Techniques Using DNS Query Data," Future Internet, MDPI, vol. 10(5), pages 1-11, May.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:5:p:43-:d:147759
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    References listed on IDEAS

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    1. Xingguo Li & Junfeng Wang & Xiaosong Zhang, 2017. "Botnet Detection Technology Based on DNS," Future Internet, MDPI, vol. 9(4), pages 1-12, September.
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    Cited by:

    1. Sangita Baruah & Dhruba Jyoti Borah & Vaskar Deka, 2023. "Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 17(1), pages 1-16, January.
    2. Simon Nam Thanh Vu & Mads Stege & Peter Issam El-Habr & Jesper Bang & Nicola Dragoni, 2021. "A Survey on Botnets: Incentives, Evolution, Detection and Current Trends," Future Internet, MDPI, vol. 13(8), pages 1-43, July.

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