E-Commerce Bot Traffic: In-Network Impact, Detection, and Mitigation

Abstract

In-network caching expedites data retrieval by storing frequently accessed data items within programmable data planes, thereby reducing data access latency. In this paper we explore a vulnerability of in-network caching to bots' traffic, showing it can significantly degrade performance. As bots constitute up to 70% of traffic on e-commerce platforms like Amazon, this is a critical problem. To mitigate the effect of bots' traffic we introduce In-network Caching Shelter (INCS), an in-network machine learning solution implemented on NVIDIA BlueField-2 DPU. Our evaluation shows that INCS can detect malicious bot traffic patterns with accuracy up to 94.72%. Furthermore, INCS takes smart actions to mitigate the effects of bot activity.