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
Link To Document :
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