Data poisoning attack detection approach for quality of service aware cloud API recommender system - Zhen CHEN; Wenchao QI; Taiyu BAO; Limin SHEN (Tongxin xuebao)
To solve the problem that existing studies usually assumed that the QoS data of cloud API recommender system was reliable, ignoring the data poisoning attack on cloud API recommender system by malicious users in open network environment, a data poisoning attack detection approach based on multi-feature fusion was proposed.Firstly, a user connected network graph was constructed based on the designed similarity function, and users’ neighborhood features were captured using Node2vec.Secondly, sparse auto-encoder was used to mine user QoS deep feature, and user interpretation feature based on QoS data weighted average deviation was designed.Furthermore, a fake user detection model based on support vector machine was established by integrating user neighborhood feature, QoS deep feature, and interpretation feature, the model parameters were learned using grid search and alternating iterative optimization strategy to complete fake user detection.Finally, the effectiveness and superiority of the proposed approach were verified through extensive experiments, realizing the poison attack defense against QoS aware cloud API recommender system at the data side.