Lessons Learned on Machine Learning for Computer Security

被引：3

作者：

Arp, Daniel ^{[1
,2
]}

Quiring, Erwin ^{[3
,4
]}

Pendlebury, Feargus ^{[2
]}

Warnecke, Alexander ^{[1
]}

Pierazzi, Fabio ^{[5
]}

Wressnegger, Christian ^{[6
,7
]}

Cavallaro, Lorenzo ^{[2
]}

Rieck, Konrad ^{[1
]}

机构：

[1] Tech Univ Berlin, Berlin, Germany

[2] UCL, London, England

[3] ICSI, Bochum, Germany

[4] Ruhr Univ Bochum, Bochum, Germany

[5] Kings Coll London, London, England

[6] KASTEL Secur Res Lab, Karlsruhe, England

[7] Karlsruhe Inst Technol, Karlsruhe, Germany

来源：

IEEE SECURITY & PRIVACY | 2023年 / 21卷 / 05期

关键词：

Privacy; Machine learning; Computer security;

D O I：

10.1109/MSEC.2023.3287207

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We identify 10 generic pitfalls that can affect the experimental outcome of AI driven solutions in computer security. We find that they are prevalent in the literature and provide recommendations for overcoming them in the future.

引用

页码：72 / 77

页数：6

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