Posted: February 20, 2018 by James Pavur
I recently gave a presentation at Rhodes House regarding the intersections between AI and cybersecurity. These are some articles, code samples, tools, etc. that I either referenced in the presentation or are related to what we discussed. Hopefully it’s useful both to those of you who were at the presentation and to anyone looking for a decent collection of resources about the relationship between AI and cybersecurity.
Main DARPA Event Site: http://archive.darpa.mil/cybergrandchallenge/
DARPA Youtube Playlist: https://www.youtube.com/playlist?list=PL6wMum5UsYvZx2x9QGhDY8j3FcQUH7uY0
Github Repository: https://github.com/jayelm/bad-flamingo
Hack Cambridge Submission: https://devpost.com/software/bad-flamingo
Academic Paper: https://arxiv.org/abs/1707.07397
Related Neural Network Walkthrough: https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721
Microsoft Official Statement: https://blogs.microsoft.com/blog/2016/03/25/learning-tays-introduction/
Cleverhans - Adversarial & Defense Toolkit: https://github.com/tensorflow/cleverhans
Useful Intro to Adversarial Examples: https://blog.openai.com/adversarial-example-research/
Discussion of Defending Against Adversarials: http://www.cleverhans.io/security/privacy/ml/2017/02/15/why-attacking-machine-learning-is-easier-than-defending-it.html
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik and A. Swami, “The Limitations of Deep Learning in Adversarial Settings,” 2016 IEEE European Symposium on Security and Privacy (EuroS&P), Saarbrucken, 2016, pp. 372-387. doi: 10.1109/EuroSP.2016.36
Papernot, Nicolas, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami. 2017. “Practical Black-Box Attacks Against Machine Learning.” In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 506–519. ASIA CCS ’17. New York, NY, USA: ACM. https://doi.org/10.1145/3052973.3053009.
J. Sahs and L. Khan, “A Machine Learning Approach to Android Malware Detection,” 2012 European Intelligence and Security Informatics Conference, Odense, 2012, pp. 141-147. doi: 10.1109/EISIC.2012.34