Data poisoning attacks on neighborhood‐based recommender systems

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Deep learning models have achieved high performance on many tasks, and thus have been applied to many security-critical scenarios. For example, deep learning-based face recognition systems have been used to authenticate users to access many security-sensitive applications like payment apps. Such usages of deep learning systems provide the adversaries with sufficient incentives to perform attack...

Evil Twins: Modeling Power Users in Attacks on Recommender Systems

Data Poisoning Attacks against Autoregressive Models

Forecasting models play a key role in money-making ventures in many different markets. Such models are often trained on data from various sources, some of which may be untrustworthy. An actor in a given market may be incentivised to drive predictions in a certain direction to their own benefit. Prior analyses of intelligent adversaries in a machine-learning context have focused on regression an...

Some Submodular Data-Poisoning Attacks on Machine Learners

The security community has long recognized the threats of data-poisoning attacks (a.k.a. causative attacks) on machine learning systems [1–6, 9, 10, 12, 16], where an attacker modifies the training data, so that the learning algorithm arrives at a “wrong” model that is useful to the attacker. To quantify the capacity and limits of such attacks, we need to know first how the attacker may modify ...

Data Poisoning Attacks on Factorization-Based Collaborative Filtering

Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in the industry, their outputs could affect business decision making, introducing incentives for an adversarial party to compromise the availability or integrity of such systems. We introduce a data poisoning attack on collaborati...