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A Siamese Neural Network for Behavioral Biometrics Authentication
We show that our approach has a few-shot classification accuracy of up to 99. 8% and 90. 8% for mobile and web interactions, respectively.
SCOPE: Secure Compiling of PLCs in Cyber-Physical Systems
We empirically measure the computational overhead caused by our approach on two experimental settings based on real CPS.
Cryptography and Security
Detection of Unauthorized IoT Devices Using Machine Learning Techniques
Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases.
