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
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