Discovery of Web Robot Sessions Based on their Navigational Patterns
Data Mining and Knowledge Discovery
HoneySpam 2.0: Profiling Web Spambot Behaviour
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
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In this paper, we describe REAL: An efficient Read Aligner for next generation sequencing reads structures to detect web Spambots. In the last decade or so, Web spam has emerged to be a bigger than previous thought problem. It not only wastes resources, misleads people but also has the ability to trick search algorithms to gain unfair search result ranking, hence resulting in the decrease of quality and reliability of the World Wide Web (WWW) and its content. New web technologies are emerging by the clock, but at the same time new spamming techniques have also emerged to misuse these technologies. Our experimental results show that the proposed system is successful for on-the-fly classification of web spambots hence eliminating spam in web 2.0 applications.