| 期刊全称 | Adversarial Machine Learning | | 影响因子2023 | Yevgeniy Vorobeychik,Murat Kantarcioglu | | 视频video | http://file.papertrans.cn/151/150410/150410.mp4 | | 学科分类 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 图书封面 |  | | 影响因子 | .The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop...The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adver | | Pindex | Book 2018 |
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