Towards Learning-Based, Content-Agnostic Detection of Social Bot Traffic

Abstract

With the fast-growing popularity of online social networks (OSNs), the security and privacy of OSN ecosystems becomes essential for the public. Among threats OSNs face, malicious social bots have become the most common and detrimental. They are often employed to violate users’ privacy, distribute spam, and disturb the financial market, posing a compelling need for effective social bot detection solutions. Unlike traditional social bot detection approaches that have strict requirements on data sources (e.g., private payload information, social relationships, or activity histories), this article proposes a method called BotFlowMon that relies only on content-agnostic flow-level data as input to identify OSN bot traffic. BotFlowMon introduces several new algorithms and techniques to classify social bot traffic from real OSN user traffic, including aggregating network flow records to obtain OSN transaction data, fusing transaction data to extract features and visualize flows, and an innovative density-valley-based clustering algorithm to subdivide each transaction into individual actions. The evaluation shows BotFlowMon can identify the traffic from social bots with a 96.1 percent accuracy, which, based on the worst case study on a testing machine, only takes no more than 0.71 seconds on average after it sees the traffic.