Unorganized Malicious Attacks Detection
This addresses a real-world security issue in recommender systems that had been understudied, though it appears incremental as it adapts existing detection frameworks to a new attack style.
The paper tackles the problem of detecting unorganized malicious attacks in recommender systems, where individual attackers use small numbers of user profiles to target different items without coordination, and proposes the UMA approach based on matrix completion, achieving effectiveness as verified theoretically and empirically.
Recommender system has 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 a different attack style: unorganized malicious attacks, where attackers individually utilize a small number of user profiles to attack different items without any organizer. This attack style occurs in many real applications, yet relevant study remains open. We first formulate the unorganized malicious attacks detection as a matrix completion problem, and propose the Unorganized Malicious Attacks detection (UMA) approach, a proximal alternating splitting augmented Lagrangian method. We verify, both theoretically and empirically, the effectiveness of our proposed approach.