TY - GEN
T1 - Defending multiple-user-multiple-target attacks in online reputation systems
AU - Liu, Yuhong
AU - Sun, Yan
AU - Yu, Ting
PY - 2011
Y1 - 2011
N2 - As online reputation systems are playing increasingly important roles in reducing risks of online interactions, attacks against such systems have evolved rapidly. Nowadays, some powerful attacks are conducted by companies that make profit through manipulating reputation of online items for their customers. These items can be products (e.g. in Amazon), businesses (e.g. hotels in travel sites), and digital content (e.g. videos in Youtube). In such attacks, colluded malicious users play wellplanned strategies to manipulate reputation of multiple target items. To address these attacks, we propose a defense scheme that (1) sets up heterogeneous thresholds for detecting suspicious items and (2) identifies target items based on correlation analysis among suspicious items. The proposed scheme and two other comparison schemes are evaluated by a combination of real user data and simulation data. The proposed scheme demonstrates significant advantages in detecting malicious users, recovering reputation scores of target items, and reducing interference to normal items.
AB - As online reputation systems are playing increasingly important roles in reducing risks of online interactions, attacks against such systems have evolved rapidly. Nowadays, some powerful attacks are conducted by companies that make profit through manipulating reputation of online items for their customers. These items can be products (e.g. in Amazon), businesses (e.g. hotels in travel sites), and digital content (e.g. videos in Youtube). In such attacks, colluded malicious users play wellplanned strategies to manipulate reputation of multiple target items. To address these attacks, we propose a defense scheme that (1) sets up heterogeneous thresholds for detecting suspicious items and (2) identifies target items based on correlation analysis among suspicious items. The proposed scheme and two other comparison schemes are evaluated by a combination of real user data and simulation data. The proposed scheme demonstrates significant advantages in detecting malicious users, recovering reputation scores of target items, and reducing interference to normal items.
UR - http://www.scopus.com/inward/record.url?scp=84862954706&partnerID=8YFLogxK
U2 - 10.1109/PASSAT/SocialCom.2011.227
DO - 10.1109/PASSAT/SocialCom.2011.227
M3 - Conference contribution
AN - SCOPUS:84862954706
SN - 9780769545783
T3 - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
SP - 425
EP - 434
BT - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
T2 - 2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011
Y2 - 9 October 2011 through 11 October 2011
ER -