Recommendation Systems (RS) have become an essential part of many online
services. Due to its pivotal role in guiding customers towards purchasing,
there is a natural motivation for unscrupulous parties to spoof RS for profits.
In this paper, we study the shilling attack: a subsistent and profitable attack
where an adversarial party injects a number of user profiles to promote or
demote a target item. Conventional shilling attack models are based on simple
heuristics that can be easily detected, or directly adopt adversarial attack
methods without a special design for RS. Moreover, the study on the attack
impact on deep learning based RS is missing in the literature, making the
effects of shilling attack against real RS doubtful. We present a novel
Augmented Shilling Attack framework (AUSH) and implement it with the idea of
Generative Adversarial Network. AUSH is capable of tailoring attacks against RS
according to budget and complex attack goals, such as targeting a specific user
group. We experimentally show that the attack impact of AUSH is noticeable on a
wide range of RS including both classic and modern deep learning based RS,
while it is virtually undetectable by the state-of-the-art attack detection
model.

本论文研究了网络推荐系统中恶意注入攻击的影响，特别是提出了一种基于生成对抗网络的新颖增强型恶意注入攻击框架（AUSH）以及攻击检测模型。实验表明 AUSH 攻击在各种推荐系统上产生了明显的影响，而且在攻击检测模型下几乎无法检测到。