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We need benchmark coverage over the most important detection problems in cybersecurity
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Classroom Contents
How We Can Power Real Security Machine Learning Progress Through Open Algorithms and Benchmarks
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- 1 Intro
- 2 What does science look like?
- 3 Security data sciences Are we doing science?
- 4 What you can do when you have openness and benchmarks
- 5 Outside of security, ML benchmarks have told the story and made the story
- 6 Cybersecurity Al vs. vision and language Al Obscurantism and hype vs. openness and progress
- 7 Security ML openness issues
- 8 Benchmark malware could be weaponized by adversaries
- 9 Protecting personal information
- 10 The risks of exposing too much to adversaries
- 11 We need benchmark coverage over the most important detection problems in cybersecurity
- 12 There are actors in the industry working on solutions, but we need more
