Detecting Avalanche Effect in Adversarial Settings: Spotting the Encryption Loops in Ransomware

2026-04-27

AI Summary

Existing approaches struggle to accurately identify encryption loops exhibiting genuine avalanche effects in ransomware that contains only binary code. This work proposes a novel anti-obfuscation and anti-evasion record-and-replay detection mechanism that, for the first time, directly verifies the avalanche effect itself rather than relying on its necessary but insufficient conditions. By integrating the Shapiro–Wilk normality test for statistical analysis, the method robustly localizes encryption loops even under adversarial conditions where input/output identification is imprecise. Experimental evaluation across ten representative ransomware families demonstrates a 0.0% false negative rate and a 1.1% false positive rate, significantly enhancing detection reliability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Security and Privacy: Applications of cryptography
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Spotting encryption loops in binary-only ransomware is a critical reverse engineering task. Since the existence of avalanche effect, an intrinsic characteristic of any secure encryption algorithms, is unavoidable during a victim data encryption attack, it is a very promising direction to spot encryption loops through avalanche effect detection. Unfortunately, no existing work in this direction ensures that the being-checked effect is the avalanche effect itself. Although CipherXRay is inspired by avalanche effect, it only checks whether a "ripple effect" (i.e., a necessary but non-sufficient condition) of avalanche effect exists, allowing a straightforward counterattack to succeed. In this work, we present a new approach that checks the avalanche effect itself. Because the detection is conducted in adversarial settings (e.g., the ransomware author may obfuscate the code), a viable approach must tolerate inaccurate input \& output identification and must be resilient to adversarial evasion. These challenges are addressed by a novel record-and-replay detection mechanism that takes advantage of the statistical guarantees provided by the Shapiro-Wilk normality test. The experimental results show that our approach achieves 0.0\% false negative rate and 1.1\% false positive rate. When our tool is employed to reverse engineer real-world ransomware samples, it succeeds in analyzing all the ransomware samples selected from ten representative families.

Problem

• avalanche effect
• ransomware
• encryption loops
• adversarial settings
• binary reverse engineering

Innovation

• avalanche effect
• adversarial detection
• record-and-replay
• ransomware analysis
• Shapiro-Wilk test