Botnet Identification on Twitter: A Novel Clustering Approach Based on Similarity

Abstract

Due to Twitter’s potential reach and influence, malicious automated accounts and services have been operating and growing without control. One of the most recognizable is the bot, a piece of programming or automated system reproducing an assignment constantly over time. The bots’ owners can also coordinate them into botnets to increase their capabilities and impact on the platform. In the literature, botnet detection on Twitter is a relatively under-researched topic where researchers need complete and updated collections, as many of the botnets currently reported are outdated or incomplete. Despite Twitter’s best efforts to mitigate the botnets’ presence by issuing restrictions and bans, they are insufficient due to their accelerated creation and expansion. Therefore, we propose a solution employing an updated botnet collection that identifies with high certainty which botnets a set of bots belongs to. Our experimentation showed that our solution labeled the botnets accurately, achieving an outstanding performance with unknown botnets in most of our experiments and when compared with other works from the literature. We also proved that our solution obtained its best scores without relying on a specific algorithm or configuration.

Keywords

• Botnet detection
• bot similarity
• clustering
• Twitter