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Discovering access-control misconfigurations: new approaches and evaluation methodologies.

, , , и . CODASPY, стр. 95-104. ACM, (2012)

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Nicola Bauer

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Measuring Real-World Accuracies and Biases in Modeling Password Guessability., , , , , , , , , и . USENIX Security Symposium, стр. 463-481. USENIX Association, (2015)Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition., , , и . CoRR, (2018)Efficient Proving for Practical Distributed Access-Control Systems., , и . ESORICS, том 4734 из Lecture Notes in Computer Science, стр. 19-37. Springer, (2007)Enforcing More with Less: Formalizing Target-Aware Run-Time Monitors., , , и . STM, том 7783 из Lecture Notes in Computer Science, стр. 17-32. Springer, (2012)OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers., , , , , , , , и . Proc. Priv. Enhancing Technol., 2022 (1): 227-252 (2022)Toward strong, usable access control for shared distributed data., , , , , , , и . FAST, стр. 89-103. USENIX, (2014)Consumable Credentials in Linear-Logic-Based Access-Control Systems., , , , и . NDSS, The Internet Society, (2007)A Spoonful of Sugar?: The Impact of Guidance and Feedback on Password-Creation Behavior., , , , , , , , , и . CHI, стр. 2903-2912. ACM, (2015)Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks., , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 13405-13430. PMLR, (2022)Practical Recommendations for Stronger, More Usable Passwords Combining Minimum-strength, Minimum-length, and Blocklist Requirements., , , и . CCS, стр. 1407-1426. ACM, (2020)