@inproceedings{dc8d9facd44d42beb61fa70668f18b06,
title = "Attacking naive bayes journal recommendation systems",
abstract = "Recommendation systems have been extensively adopted in various applications. However, with the security concern of artificial intelligence, the robustness of such systems against malicious attacks has been studied in recent years. In this paper, we build a journal recommendation system based on the Naive Bayesian algorithm which helps recommend suitable journals for the authors. Since journal recommendation systems may also suffer from various attacks, we explore attack methods on the malicious data. We construct specific malicious data to attack the availability of training data, and such deviations in the training data could lead to poor recommendation accuracy. We also conduct extensive experiments and the results show that the recommendation accuracy could be dramatically reduced under such attacks.",
keywords = "Journal recommendation systems, Malicious data attack, Naive Bayes",
author = "Sheng Wang and Mohan Li and Yinyin Cai and Zhaoquan Gu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2020.; 6th International Conference on Artificial Intelligence and Security,ICAIS 2020 ; Conference date: 17-07-2020 Through 20-07-2020",
year = "2020",
doi = "10.1007/978-981-15-8101-4\_12",
language = "英语",
isbn = "9789811581007",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "118--128",
editor = "Xingming Sun and Jinwei Wang and Elisa Bertino",
booktitle = "Artificial Intelligence and Security - 6th International Conference, ICAIS 2020, Proceedings",
address = "德国",
}