@inproceedings{a33e7c1054f44dc58e29f1ee9d3c6d31,
title = "Randomizing SVM against adversarial attacks under uncertainty",
abstract = "Robust machine learning algorithms have been widely studied in adversarial environments where the adversary maliciously manipulates data samples to evade security systems. In this paper, we propose randomized SVMs against generalized adversarial attacks under uncertainty, through learning a classifier distribution rather than a single classifier in traditional robust SVMs. The randomized SVMs have advantages on better resistance against attacks while preserving high accuracy of classification, especially for non-separable cases. The experimental results demonstrate the effectiveness of our proposed models on defending against various attacks, including aggressive attacks with uncertainty.",
keywords = "Adversarial learning, Randomization, Robust SVM",
author = "Yan Chen and Wei Wang and Xiangliang Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 ; Conference date: 03-06-2018 Through 06-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93040-4_44",
language = "English (US)",
isbn = "9783319930398",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "556--568",
editor = "Webb, {Geoffrey I.} and Dinh Phung and Mohadeseh Ganji and Lida Rashidi and Tseng, {Vincent S.} and Bao Ho",
booktitle = "Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings",
address = "Germany",
}