Web Spambot Detection Based on Web Navigation Behaviour

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Web Spambot Detection Based on Web Navigation Behaviour. Pedram Hayati Vidyasagar Potdar Kevin Chai Alex Talevski Anti-Spam Research Lab (ASRL) Digital Ecosystem and Business Intelligence Institute Curtin University, Perth, Western Australia. Introduction. - PowerPoint PPT Presentation

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  • Web Spambot Detection Based on Web Navigation BehaviourPedram HayatiVidyasagar PotdarKevin ChaiAlex Talevski

    Anti-Spam Research Lab (ASRL)Digital Ecosystem and Business Intelligence InstituteCurtin University, Perth, Western Australia

    *www.AntiSpamResearchLab.com

    IntroductionJunk, Unrelated, Unwelcome, Anonymous content ==> spam.Spam now not only spreads through email but also through Web 2.0.This new trend of spamming is called as Spam 2.0.

    *www.AntiSpamResearchLab.com

    Examples of Spam 2.0Hosting Spam content in Web applications on legitimate websites. P. Hayati, V. Potdar, A. Talveski, N. Firoozeh, S. Sarenche, E. A. Yeganeh. Spam 2.0 Definition, New Spamming Boom. DEST 2010, Dubai, UAE, April 2010.

    *www.AntiSpamResearchLab.com

    Web SpamBotA tool is used by spammer to distribute Spam 2.0.Use the idea of Web robots.Mimic Human user behaviour.Waste useful resources.

    In order to counter Spam 2.0 We can concentrate on Web Spambot detection as Source of Spam 2.0 problem.

    *www.AntiSpamResearchLab.com

    Spam 2.0

    *www.AntiSpamResearchLab.com

    CountermeasuresMostly on Email Spam detection.Content based, Meta-Content based.Applicable for Web environment like link-based detection.CAPTCHAPossible to bypass using ML.Machines are better to decipher.Inconveniences human users.

    *www.AntiSpamResearchLab.com

    ProblemNot suitable for web 2.0 platformSpam hosts on legitimate website Parasitic natureWe cannot make whole website blacklisted because of spam posts.

    *www.AntiSpamResearchLab.com

    Our SolutionStudy Web spambot behaviour in order to stop spam 2.0.Fundamental assumption: spambot behaviour is intrinsically different from those of humans.Use Web Usage Data.Contain information about user navigation through website.Can be gathered implicitly.Convert web usage data into a format that can beExtendibleDiscriminative

    *www.AntiSpamResearchLab.com

    Our SolutionPropose new feature set called Action.a set of user requested webpages to achieve a certain goal.Examplein an online forum, a user navigates to a specific board then goes to the New Thread page to start a new topic. This user navigation can be formulated as submitting new content action.

    *www.AntiSpamResearchLab.com

    Framework

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    Action Extraction

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    Algorithm

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    Dataset60 days study of web spambot behaviour on a live discussion board (HoneySpam 2.0 Project).1 month study of human user behaviour.

    *www.AntiSpamResearchLab.com

    Action Frequency of Humans and Spambots

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    Performance MeasurementMatthew Correlation Coefficient (MCC)

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    Results

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    ConclusionWe propose innovative idea by focusing on spambot identification to manage spam rather than analysing spam content.We proposed a novel framework to detect spambots inside Web 2.0 applications, which lead us to Spam 2.0 detection.We proposed a new feature set i.e. action navigations, to detect spambots.We validated our framework against an online forum and achieved 96.24% accuracy using the MCC method

    *www.AntiSpamResearchLab.com

    Thank YOU!Web Spambot Detection Based on Web Navigation BehaviourPedram Hayati p.hayati@curtin.edu.auVidyasagar Potdar v.potdar@curtin.edu.auKevin Chai k.chai@curtin.edu.auAlex Talevski a.talevski@curtin.edu.au

    Anti-Spam Research Lab (ASRL)Digital Ecosystem and Business Intelligence InstituteCurtin University, Perth, Western Australia

    www.antispamresearchlab.com

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