Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation

Steve T.K. Jan, Qingying Hao, Tianrui Hu, Jiameng Pu, Sonal Oswal, Gang Wang, Bimal Viswanath

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Machine learning has been widely applied to building security applications. However, many machine learning models require the continuous supply of representative labeled data for training, which limits the models' usefulness in practice. In this paper, we use bot detection as an example to explore the use of data synthesis to address this problem. We collected the network traffic from 3 online services in three different months within a year (23 million network requests). We develop a stream-based feature encoding scheme to support machine learning models for detecting advanced bots. The key novelty is that our model detects bots with extremely limited labeled data. We propose a data synthesis method to synthesize unseen (or future) bot behavior distributions. The synthesis method is distribution-aware, using two different generators in a Generative Adversarial Network to synthesize data for the clustered regions and the outlier regions in the feature space. We evaluate this idea and show our method can train a model that outperforms existing methods with only 1% of the labeled data. We show that data synthesis also improves the model's sustainability over time and speeds up the retraining. Finally, we compare data synthesis and adversarial retraining and show they can work complementary with each other to improve the model generalizability.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Symposium on Security and Privacy, SP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages17
ISBN (Electronic)9781728134970
StatePublished - May 2020
Event41st IEEE Symposium on Security and Privacy, SP 2020 - San Francisco, United States
Duration: May 18 2020May 21 2020

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
ISSN (Print)1081-6011


Conference41st IEEE Symposium on Security and Privacy, SP 2020
Country/TerritoryUnited States
CitySan Francisco

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Software
  • Computer Networks and Communications


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