Five Essential Machine Learning Security Papers

We recently published "Practical Attacks on Machine Learning Systems", which has a very large references section - possibly too large - so we've boiled down the list to five papers that are absolutely essential in this area. If you're beginning your journey in ML security, and have the very basics down, these papers are a … Continue reading Five Essential Machine Learning Security Papers

Whitepaper – Practical Attacks on Machine Learning Systems

This paper collects a set of notes and research projects conducted by NCC Group on the topic of the security of Machine Learning (ML) systems. The objective is to provide some industry perspective to the academic community, while collating helpful references for security practitioners, to enable more effective security auditing and security-focused code review of ML systems. Details of specific practical attacks and common security problems are described. Some general background information on the broader subject of ML is also included, mostly for context, to ensure that explanations of attack scenarios are clear, and some notes on frameworks and development processes are provided.

Machine Learning for Static Analysis of Malware – Expansion of Research Scope

Introduction The work presented in this blog post is that of Ewan Alexander Miles (former UCL MSci student) and explores the expansion of scope for using machine learning models on PE (portable executable) header files to identify and classify malware. It is built on work previously presented by NCC Group, in conjunction with UCL’S Centre … Continue reading Machine Learning for Static Analysis of Malware – Expansion of Research Scope

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

Incremental Machine Learning by Example: Detecting Suspicious Activity with Zeek Data Streams, River, and JA3 Hashes

tl:dr Incremental Learning is an extremely useful machine learning paradigm for deriving insight into cyber security datasets. This post provides a simple example involving JA3 hashes showing how some of the foundational algorithms that enable incremental learning techniques can be applied to novelty detection (the first time something has happened) and outlier detection (rare events) … Continue reading Incremental Machine Learning by Example: Detecting Suspicious Activity with Zeek Data Streams, River, and JA3 Hashes

Machine learning from idea to reality: a PowerShell case study

Detecting both ‘offensive’ and obfuscated PowerShell scripts in Splunk using Windows Event Log 4104 This blog provides a ‘look behind the scenes’ at the RIFT Data Science team and describes the process of moving from the need or an idea for research towards models that can be used in practice. More specifically, how known and … Continue reading Machine learning from idea to reality: a PowerShell case study

Project Ava: On the Matter of Using Machine Learning for Web Application Security Testing – Part 10: Efficacy Demonstration, Project Conclusion and Next Steps

After 400 days of research, the Project Ava team round up their conclusions on whether machine learning could ever be harnessed to complement current pentesting capabilities. Read more to uncover the team’s verdict on whether this will ever be possible in the near future... Overview Having spent almost 400 people days of research effort on … Continue reading Project Ava: On the Matter of Using Machine Learning for Web Application Security Testing – Part 10: Efficacy Demonstration, Project Conclusion and Next Steps