A Novel Scheme for Bot Detection in Online Social Media: BotDefender

  • Neharika Singh
  • Madhumita ChatterjeeEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


These days online informal organisation is the well-known and productive stage medium for billions of clients. With the data innovation and systems administration several billions of dynamic clients all around the globe are utilizing the web interpersonal organisations such as Facebook, Twitter, LinkedIn and so on. Online Social Media (OSM) gave platforms to the client to interface, impart and communicate with other individuals. In this paper, online interpersonal organisations (OSN) enlist bots and utilizes photo/imaging frameworks to communicate as corresponding channels between bots. Botnet is the principal that uses the OSN stage as a way to control cell bots. The structure and attributes of OSN make this bot harder to recognize, resilient to bot failure and more cost-effective. Our goal is to bring issues to enlight new botnet that endeavor OSN to enroll bots so that preventive measures can be executed to stop this sort of assault later.


Online Social Media Online social network Social network bots Botnet detection BotDefender 


  1. 1.
    Binkley, J.R., Singh, S.: An Algorithm for Anomaly-Based Botnet Detection. Global Information Assurance Certification (GIAC), 8 August 2014Google Scholar
  2. 2.
    Ji, Y., He, Y., Jiang, X., Li, Q.: Towards Social Botnet Behavior Detection in the End Host. IEEE (2014)Google Scholar
  3. 3.
    Sharma, R., Deepshikha: Social networking sites: a new platform for botnets a short case study to prove that how today’s social networking is a new platform for cyber criminals. Int. J. Emerg. Technol. Adv. Eng. 4(1) (2014)Google Scholar
  4. 4.
    Venkatachalam, N., Anitha, R.: A multi-feature approach to detect Stegobot: a covert multimedia social network botnet. Springer (2016)Google Scholar
  5. 5.
    Ghanadi, M., Abadi, M.: SocialClymene: A Negative Reputation System for Covert Botnet Detection in Social Network. IEEE (2014)Google Scholar
  6. 6.
    Rahman, M.S., Huang, T.K., Madhyastha, H.V., Faloutsos, M.: FRAppE: Detecting Malicious Facebook Applications. IEEE (2012)Google Scholar
  7. 7.
    Ahmadizadeh, E., Aghasian, E., Taheri, H.P., Nejad, R.F.: An Automated Model to Detect Fake Profiles and botnets in online social network using steganography technique. IOSR J. Comput. Eng. 17(1) (2015)Google Scholar
  8. 8.
    Natarajan, V., Sheen, S., Anitha, R.: Multilevel analysis to detect covert social botnet in multimedia social networks. Comput. J. 58 (2015)CrossRefGoogle Scholar
  9. 9.
    Zhang, J., Lee, W.: Botsniffer: detecting Botnet command and control channels in network traffic. In: Network and Distributed System Security Symposium (NDSS) (2008)Google Scholar
  10. 10.
    Freiling, F.C., Holz, T., Wicherski, G.: Botnet tracking: exploring a root cause methodology to prevent Distributed Denial of Service Attacks. In: European Symposium of Research in Computer Security (ESORICS) (2005)CrossRefGoogle Scholar
  11. 11.
    Zhang, J., Gu, G.: BotMiner: clustering analysis of network traffic for protocol and structured independent Botnet detection. In: Distributed Framework and Application (DFMA) (2008)Google Scholar
  12. 12.
    Carbone, R., Gibbs, P.M.: Botnet tracking tool. In: Global Information Assurance Certification (GIAC), 8 August 2014Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Engineering, PCEMumbai UniversityNavi MumbaiIndia

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