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Poster
Unorganized Malicious Attacks Detection
Ming Pang · Wei Gao · Min Tao · Zhi-Hua Zhou

Wed Dec 05 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #143
in Wed Poster Session A »

Recommender systems have attracted much attention during the past decade. Many attack detection algorithms have been developed for better recommendations, mostly focusing on shilling attacks, where an attack organizer produces a large number of user profiles by the same strategy to promote or demote an item. This work considers another different attack style: unorganized malicious attacks, where attackers individually utilize a small number of user profiles to attack different items without organizer. This attack style occurs in many real applications, yet relevant study remains open. We formulate the unorganized malicious attacks detection as a matrix completion problem, and propose the Unorganized Malicious Attacks detection (UMA) algorithm, based on the alternating splitting augmented Lagrangian method. We verify, both theoretically and empirically, the effectiveness of the proposed approach.

Keywords: Ranking and Preference Learning · Recommender Systems · Privacy, Anonymity, and Security
[ Paper] 

Author Information

Ming Pang (Nanjing University)

Currently I am a second year PH.D. student of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group, led by Prof. Zhi-Hua Zhou. I received my B.Sc. degree in Computer Science and Technology in June 2014 from Nanjing University. Since September 2014, I was admitted to study for a Ph.D. degree in Nanjing University.

Wei Gao (Nanjing University)
Min Tao (Nanjing University)
Zhi-Hua Zhou (Nanjing University)

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