A New Ransomware Detection Method Based on Case-Based Reasoning and Diffusion Models

This article introduces a new open-source ransomware detection project that combines case-based reasoning (CBR) and diffusion model technologies. This method aims to solve the problem that traditional signature-based detection struggles to deal with rapidly evolving ransomware variants, and has advantages such as adaptability, interpretability, and evolvability. It shows broad application prospects in fields like enterprise endpoint security, cloud security, and threat intelligence.

Ransomware has become one of the most destructive threats in the cybersecurity field. From WannaCry to NotPetya, attacks have caused huge economic losses and threatened critical infrastructure. Traditional signature-based detection methods are difficult to deal with variants (code obfuscation, encryption shells, polymorphic deformation) and zero-day vulnerability exploitation, so there is an urgent need for intelligent and adaptive detection technologies.

The core of the project is the innovative integration of case-based reasoning and diffusion models:

• Case-Based Reasoning (CBR) : Solve problems based on experience, retrieve historically similar cases to judge the maliciousness of new samples;
• Diffusion Models : Introduced from the field of generative AI, used for feature learning (distinguishing feature distributions between normal and malicious software) and data augmentation (generating synthetic samples to expand training data).

The detection framework consists of three layers:

1. Feature Extraction Layer: Extracts static features (file structure, code snippets) and dynamic features (API calls, network behavior, file operations);
2. Case Library Management Layer: Maintains a structured historical case database (including feature vectors, labels, family attribution) and adopts an incremental update strategy;
3. Diffusion Enhancement Layer: Learns feature distribution boundaries to distinguish ambiguous samples, and generates synthetic samples to enhance the learning of rare families.

Detection process steps:

1. After sample submission, static analysis + dynamic sandbox execution are performed to extract features;
2. Case retrieval (approximate nearest neighbor search) finds the Top-K similar cases for preliminary classification;
3. Ambiguous samples trigger diffusion enhancement analysis, and anomalies are judged through reconstruction errors;
4. A threat score and report are given based on a combination of similarity, reconstruction error, etc.

Technical advantages:

• Adaptability: No retraining required; adding new cases to the library can adapt to new threats;
• Interpretability: Provides similar case basis to help analysts verify results;
• Evolvability: Case library accumulation enhances capabilities, and diffusion models alleviate class imbalance.

Application scenarios:

• Enterprise endpoint security: As an enhancement layer for traditional antivirus software;
• Cloud security: Intelligent initial screening of large-scale samples;
• Threat intelligence: Family classification and evolution tracking;
• IoT/edge computing: Adapt to resource-constrained environments to provide lightweight protection.

This method represents a new direction for AI applications in cybersecurity, using interdisciplinary integration to solve practical problems. A single technology is difficult to deal with ransomware evolution, and an intelligent system combining multiple AI technologies provides new possibilities for building a robust defense system. It is expected that the open-source project will receive community participation for improvement, helping to combat cybercrime.