Biblio

Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.

With the proliferation of Android-based devices, malicious apps have increasingly found their way to user devices. Many solutions for Android malware detection rely on machine learning; although effective, these are vulnerable to attacks from adversaries who wish to subvert these algorithms and allow malicious apps to evade detection. In this work, we present a statistical analysis of the impact of adversarial evasion attacks on various linear and non-linear classifiers, using a recently proposed Android malware classifier as a case study. We systematically explore the complete space of possible attacks varying in the adversary's knowledge about the classifier; our results show that it is possible to subvert linear classifiers (Support Vector Machines and Logistic Regression) by perturbing only a few features of malicious apps, with more knowledgeable adversaries degrading the classifier's detection rate from 100% to 0% and a completely blind adversary able to lower it to 12%. We show non-linear classifiers (Random Forest and Neural Network) to be more resilient to these attacks. We conclude our study with recommendations for designing classifiers to be more robust to the attacks presented in our work.