Ransomware remains a serious threat to macOS users, especially as Apple devices continue to gain traction in the enterprise. Despite its many variants, most ransomware shares a fundamental behaviour: rapidly transforming user files into encrypted data.
This talk presents a practical, maths-driven approach to generically detecting and stopping ransomware on macOS. While this approach builds on established ideas, it is now far more practical with Apple's Endpoint Security framework. By leveraging Endpoint Security and entropy-based analysis, we can distinguish benign file activity from encryption and further differentiate encrypted data from compressed data using simple mathematical heuristics. This approach avoids signatures and machine learning, instead relying on observable behaviour and measurable properties of data.
We will walk through the implementation in a free, open-source tool, including real-world testing, and demonstrate the approach against macOS ransomware samples. We will also acknowledge limitations and possible avenues for evasion.
The result is a lightweight, explainable, and practical method for detecting ransomware on macOS using maths, not AI.