Machine learning has shown great success in building security applications including bot detection. However, many machine learning models are difficult to deploy since model training requires the continuous supply of representative labeled data, which are expensive and time-consuming to obtain in practice. In this thesis, we build a bot detection system with a data synthesis method to explore detecting bots with limited data 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 novel stream-based feature encoding scheme to support our model to perform real-time bot detection on anonymized network data. We propose a data synthesis method to synthesize unseen (or future) bot behavior distributions to enable our system to detect bots with extremely limited labeled data. 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. / Master of Science / An internet bot is a computer-controlled software performing simple and automated tasks over the internet. Although some bots are legitimate, many bots are operated to perform malicious behaviors causing severe security and privacy issues. To address this problem, machine learning (ML) models that have shown great success in building security applications are widely used in detecting bots since they can identify hidden patterns learning from data. However, many ML-based approaches are difficult to deploy since model training requires labeled data, which are expensive and time-consuming to obtain in practice, especially for security tasks. Meanwhile, the dynamic-changing nature of malicious bots means bot detection models need the continuous supply of representative labeled data to keep the models up-to-date, which makes bot detection more challenging. In this thesis, we build an ML-based bot detection system to detect advanced malicious bots in real-time by processing network traffic data. We explore using a data synthesis method to detect bots with limited training data to address the limited and unrepresentative labeled data problem. Our proposed data synthesis method synthesizes unseen (or future) bot behavior distributions to enable our system to detect bots with extremely limited labeled data. We evaluate our approach using real-world datasets we collected and show that our model outperforms existing methods using only 1% of the labeled data. We show that data synthesis also improves the model's sustainability over time and helps to keep it up-to-date easier. Finally, we show that our method can work complementary with adversarial retraining to improve the model generalizability.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/98595 |
| Date | 28 May 2020 |
| Creators | Hu, Tianrui |
| Contributors | Computer Science, Viswanath, Bimal, Wang, Gang Alan, Huang, Bert |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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