Adversarial attacks on machine learning systems for high-frequency trading
Adversarial attacks on machine learning systems for high-frequency trading
复制标题
针对高频交易的机器学习系统的对抗性攻击
DOI：
10.1145/3490354.3494367
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
T. Goldstein
中科院分区：
文献类型：
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作者：
Micah Goldblum;Avi Schwarzschild;N. Cohen;T. Balch;Ankit B. Patel;T. Goldstein
算法交易系统通常是完全自动化的，深度学习在该领域越来越受到关注。尽管如此，人们对这些模型的鲁棒性特性知之甚少。我们从对抗性机器学习的角度研究算法交易的估值模型。我们引入了特定于该域的新攻击，并具有规模限制，可最大限度地降低攻击成本。我们进一步讨论如何将这些攻击用作分析工具来研究和评估金融模型的稳健性。最后，我们研究了现实对抗性攻击的可行性，其中对抗性交易者欺骗自动交易系统做出不准确的预测。
Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness properties of these models. We study valuation models for algorithmic trading from the perspective of adversarial machine learning. We introduce new attacks specific to this domain with size constraints that minimize attack costs. We further discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models. Finally, we investigate the feasibility of realistic adversarial attacks in which an adversarial trader fools automated trading systems into making inaccurate predictions.
DOI：
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发表时间：
2018-06
期刊：
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影响因子：
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作者：
H. Dai;Hui Li-;Tian Tian-Tian;Xin Huang;L. Wang;Jun Zhu;Le Song
通讯作者：
H. Dai;Hui Li-;Tian Tian-Tian;Xin Huang;L. Wang;Jun Zhu;Le Song
DOI：
10.1609/aaai.v34i04.6017
发表时间：
2018-11
期刊：
ArXiv
影响因子：
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作者：
Ali Shafahi;Mahyar Najibi;Zheng Xu;John P. Dickerson;L. Davis;T. Goldstein
通讯作者：
Ali Shafahi;Mahyar Najibi;Zheng Xu;John P. Dickerson;L. Davis;T. Goldstein