The LinUCB algorithm is a typical algorithm for solving the contextual multi-armed bandit problem,which is widely used in scenarios such as news delivery,product recommendation,and medical resource allocation.There is very little research on the security of this algorithm,which requires further investigation of their attack methods in order to make targeted and even universal defense measures.In this work,we first propose two attack schemes for offline data poisoning attacks on the LinUCB algorithm by adding fake data,namely TCA(target context attack)and OCA(optimized context attack).The former generates poisoning data based on the similarity between training data and target context,while the latter models an optimization problem to construct the poisoning data,which is an optimized version of the former.Experimental evaluations show that only by adding a small amount of poisoning data we could achieve a 100％attack success rate.
contextual multi-armed banditLinUCBdata poisoning attackwhite-box attackoptimization problem
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