无组织恶意攻击检测
Unorganized Malicious Attacks Detection
摘要：
推荐系统在过去的十年里引起了人们的广泛关注。已经开发了许多攻击检测算法以用于更好的推荐，主要集中于先令攻击，其中攻击组织者通过相同的策略产生大量的用户简档以提升或降级项目。这项工作考虑了一种不同的攻击风格：无组织的恶意攻击，攻击者单独利用少量的用户简档攻击不同的项目，没有任何组织者。这种攻击方式在很多实际应用中都有发生，但相关的研究还没有展开。我们首先制定了无组织的恶意攻击检测矩阵完成问题，并提出了无组织的恶意攻击检测（UMA）的方法，一个近端交替分裂增广拉格朗日方法。我们从理论上和经验上验证了我们所提出的方法的有效性。
英文摘要：
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.
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