Abstract:
Web bots are programs that can be used to browse the web and perform automated actions. These actions can be benign, such as web indexing and website monitoring, or malic...Show MoreMetadata
Abstract:
Web bots are programs that can be used to browse the web and perform automated actions. These actions can be benign, such as web indexing and website monitoring, or malicious, such as unauthorised content scraping and scalping. To detect bots, web servers consider bots’ fingerprint and behaviour, with research showing that techniques that examine the visitor’s mouse movements can be very effective. In this work, we showcase that web bots can leverage the latest advances in machine learning to evade detection based on their mouse movements and touchscreen trajectories (for the case of mobile web bots). More specifically, the proposed web bots utilise Generative Adversarial Networks (GANs) to generate images of trajectories similar to those of humans, which can then be used by bots to evade detection. We show that, even if the web server is aware of the attack method, web bots can generate behaviours that can evade detection.
Date of Conference: 26-28 July 2021
Date Added to IEEE Xplore: 06 September 2021
ISBN Information:
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- IEEE Keywords
- Index Terms
- Generative Adversarial Networks ,
- Bot Accounts ,
- Bot Detection ,
- Machine Learning ,
- Web Server ,
- Browsing ,
- Advanced Machine Learning ,
- Mouse Movements ,
- Case Trajectories ,
- True Positive ,
- Convolutional Neural Network ,
- Mobile Devices ,
- Generation Of Mice ,
- Number Of Images ,
- Detection Model ,
- TensorFlow ,
- Web Page ,
- Image Generation ,
- Detection Module ,
- Reinforcement Learning Techniques ,
- Trajectory Generation ,
- Detection Framework ,
- Human-like Behavior ,
- Real Ones ,
- Accelerometer Data ,
- Movement Generation ,
- Performance Of Framework
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Generative Adversarial Networks ,
- Bot Accounts ,
- Bot Detection ,
- Machine Learning ,
- Web Server ,
- Browsing ,
- Advanced Machine Learning ,
- Mouse Movements ,
- Case Trajectories ,
- True Positive ,
- Convolutional Neural Network ,
- Mobile Devices ,
- Generation Of Mice ,
- Number Of Images ,
- Detection Model ,
- TensorFlow ,
- Web Page ,
- Image Generation ,
- Detection Module ,
- Reinforcement Learning Techniques ,
- Trajectory Generation ,
- Detection Framework ,
- Human-like Behavior ,
- Real Ones ,
- Accelerometer Data ,
- Movement Generation ,
- Performance Of Framework
- Author Keywords