• DocumentCode
    62809
  • Title

    Anti-Reconnaissance Tools: Detecting Targeted Socialbots

  • Author

    Paradise, A. ; Puzis, Rami ; Shabtai, Asaf

  • Author_Institution
    Dept. of Inf. Syst. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
  • Volume
    18
  • Issue
    5
  • fYear
    2014
  • fDate
    Sept.-Oct. 2014
  • Firstpage
    11
  • Lastpage
    19
  • Abstract
    Advanced attackers use online social networks to extract useful information about the target organization, including its members and their connections, affiliations, and positions. Socialbots are artificial, machine-operated, social network profiles that connect to real members of an organization, greatly increasing the amount of information an attacker can collect. To connect socialbots, attackers can employ several strategies. The authors´ approach hunts socialbots using a carefully chosen monitoring strategy by intelligently selecting organization member profiles and monitoring their activity. Their results demonstrate their method´s efficacy-specifically, when attackers know the defense strategy being deployed, the attack they will most likely use is randomly sprayed friend requests, which eventually lead to a low number of connections.
  • Keywords
    security of data; social networking (online); activity monitoring; antireconnaissance tools; artificial machine-operated social network profiles; intelligent organization member profile selection; randomly sprayed friend requests; targeted socialbot detection; Information retrieval; Internet; Mathematical model; Online services; Social network services; Targeting; reconnaissance; social network; socialbots;
  • fLanguage
    English
  • Journal_Title
    Internet Computing, IEEE
  • Publisher
    ieee
  • ISSN
    1089-7801
  • Type

    jour

  • DOI
    10.1109/MIC.2014.81
  • Filename
    6840822