Rule-based on-the-fly web spambot detection using action strings

Web spambots are a new type of internet robot that spread spam content through Web 2.0 applications like online discussion boards, blogs, wikis, social networking platforms etc. These robots are intelligently designed to act like humans in order to fool safeguards and other users. Such spam content not only wastes valuable resources and time but also may mislead users with unsolicited content. Spam content typically intends to misinform users (scams), generate traffic, make sales (marketing/advertising), and occasionally compromise parties, people or systems by spreading spyware or malwares. Current countermeasures do not effectively identify and prevent web spambots. Proactive measures to deter spambots from entering a site are limited to question/response scenarios. The remaining efforts then focus on spam content identification as a passive activity. Spammers have evolved their techniques to bypass existing anti-spam filters. In this paper, we describe a rule-based web usage behaviour action string that can be analysed using Trie data structures to detect web spambots. Our experimental results show the proposed system is successful for on-the-fly classification of web spambots hence eliminating spam in web 2.0 applications.