Cracking Random Number Generators using Machine Learning – Part 2: Mersenne Twister

Outline 1. Introduction2. How does MT19937 PRNG work?3. Using Neural Networks to model the MT19937 PRNG3.1 Using NN for State Twisting3.1.1 Data Preparation3.1.2 Neural Network Model Design3.1.3 Optimizing the NN Inputs3.1.4 Model Results3.1.5 Model Deep Dive3.1.5.1 Model First Layer Connections3.1.5.2 The Logic Closed-Form from the State Twisting Model Output3.2 Using NN for State Tempering3.2.1 Data Preparation3.2.2 … Continue reading Cracking Random Number Generators using Machine Learning – Part 2: Mersenne Twister

Cracking Random Number Generators using Machine Learning – Part 1: xorshift128

Outline 1. Introduction2. How does xorshift128 PRNG work?3. Neural Networks and XOR gates4. Using Neural Networks to model the xorshift128 PRNG4.1 Neural Network Model Design4.2 Model Results4.3 Model Deep Dive5. Creating a machine-learning-resistant version of xorshift1286. Conclusion 1. Introduction This blog post proposes an approach to crack Pseudo-Random Number Generators (PRNGs) using machine learning. By cracking … Continue reading Cracking Random Number Generators using Machine Learning – Part 1: xorshift128

Research Paper – Machine Learning for Static Malware Analysis, with University College London

For the past few years, NCC Group has been an industry partner to the Centre for Doctoral Training in Data Intensive Science (CDT in DIS) at University College London (UCL). CDT is composed of a group of over 80 academics from across UCL in areas such as High Energy Physics, Astrophysics, Atomic and Molecular Physics, … Continue reading Research Paper – Machine Learning for Static Malware Analysis, with University College London