Unorganized Malicious Attacks Detection

TL;DR

This paper introduces UMA, a novel method for detecting unorganized malicious attacks in recommender systems by formulating the problem as matrix completion and solving it with an augmented Lagrangian approach, addressing a previously under-studied attack style.

Contribution

The paper proposes the first detection method for unorganized malicious attacks using matrix completion and an augmented Lagrangian algorithm, filling a gap in attack detection research.

Findings

UMA effectively detects unorganized malicious attacks.

Theoretical analysis confirms the method's convergence.

Empirical results demonstrate high detection accuracy.

Abstract

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.