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When the Magic Wears Off - Flaws in ML for Security Evaluations and What to Do About It

When the Magic Wears Off - Flaws in ML for Security Evaluations and What to Do About It

Security BSides London via YouTube Direct link

Conclusion

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11 of 11

Conclusion

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When the Magic Wears Off - Flaws in ML for Security Evaluations and What to Do About It

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  1. 1 Intro
  2. 2 ML for Security
  3. 3 The magic of cross-validation
  4. 4 The curse of cross-validation
  5. 5 Temporally inconsistent datasets
  6. 6 Time Decay
  7. 7 Bias From Imbalanced Testing
  8. 8 Tuning the Training Ratio
  9. 9 Evaluation Constraints
  10. 10 Discussion (2/2)
  11. 11 Conclusion

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