TY - GEN
T1 - A trust-based detecting mechanism against profile injection attacks in recommender systems
AU - Zhang, Qiang
AU - Luo, Yuan
AU - Weng, Chuliang
AU - Li, Minglu
PY - 2009
Y1 - 2009
N2 - Recommender systems could be applied in grid environment to help grid users select more suitable services by making high quality personalized recommendations. Also, recommendation could be employed in the virtual machines managing platform to measure the performance and creditability of each virtual machine. However, such systems have been shown to be vulnerable to profile injection attacks (shilling attacks), attacks that involve the insertion of malicious profiles into the ratings database for the purpose of altering the system's recommendation behavior. In this paper we introduce and evaluate a new trust-based detecting algorithm for protecting recommender systems against profile injection attacks. Moreover, we discuss the combination of our trust-based metrics with previous metrics such as RDMA in profile-level and item-level respectively. In the end, we show these metrics can lead to improved detecting accuracy experimentally.
AB - Recommender systems could be applied in grid environment to help grid users select more suitable services by making high quality personalized recommendations. Also, recommendation could be employed in the virtual machines managing platform to measure the performance and creditability of each virtual machine. However, such systems have been shown to be vulnerable to profile injection attacks (shilling attacks), attacks that involve the insertion of malicious profiles into the ratings database for the purpose of altering the system's recommendation behavior. In this paper we introduce and evaluate a new trust-based detecting algorithm for protecting recommender systems against profile injection attacks. Moreover, we discuss the combination of our trust-based metrics with previous metrics such as RDMA in profile-level and item-level respectively. In the end, we show these metrics can lead to improved detecting accuracy experimentally.
UR - https://www.scopus.com/pages/publications/72849126409
U2 - 10.1109/SSIRI.2009.12
DO - 10.1109/SSIRI.2009.12
M3 - 会议稿件
AN - SCOPUS:72849126409
SN - 9780769537580
T3 - SSIRI 2009 - 3rd IEEE International Conference on Secure Software Integration Reliability Improvement
SP - 59
EP - 64
BT - SSIRI 2009 - 3rd IEEE International Conference on Secure Software Integration Reliability Improvement
T2 - 3rd IEEE International Conference on Secure Software Integration Reliability Improvement, SSIRI 2009
Y2 - 8 July 2009 through 10 July 2009
ER -