Deep Learning Decoy Files for Ransomware Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for preventing ransomware attacks on file servers are ineffective due to intelligent ransomware variants that can differentiate decoy files from user files, evade detection in virtual environments, and exploit trusted applications, leading to frequent failures in early-stage detection and backup encryption.
Innovation Solution
A deep learning engine is deployed to generate decoy files by extracting features from user files, converting them into vector format, estimating errors, and creating decoy files if the error is below a threshold, which misdirects ransomware attacks and alerts the trap layer to prevent file corruption, using LSTM neural networks and decoy processes to mimic user file behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If canary files (decoy files) are used to monitor file activities, then ransomware detection capability is improved, but the detection reliability deteriorates because intelligent ransomware variants can identify and differentiate decoy files from user files
Solution Approach 1:
The patent changes the parameters of decoy files by generating them with specific entropy values and metadata characteristics that match user files. The deep learning engine analyzes file features and generates decoys with adjusted parameters (entropy, metadata) to evade ransomware differentiation, thereby maintaining detection reliability while improving detection capability.
2Difficulty of detecting and measuring
If dynamic analysis tools are used to test files in virtual environments, then file categorization is improved, but the detection reliability deteriorates because malicious files can sense the virtual environment and exhibit genuine behavior
Solution Approach 1:
The patent introduces an intermediary approach by using a deep learning engine as a mediator between file analysis and categorization. Instead of directly analyzing files in virtual environments where malware can disguise its behavior, the deep learning engine processes file features and generates predictions, serving as an intermediary that maintains detection reliability while improving categorization capability.
3Difficulty of detecting and measuring
If static analysis techniques are used to detect cryptographic algorithms, then detection capability is improved, but the reliability deteriorates because code obfuscation methods challenge detection accuracy
Solution Approach 1:
The patent applies parameter changes by using a deep learning engine to analyze cryptographic algorithms dynamically. The system changes from static analysis parameters to dynamic feature extraction, where the deep learning model processes algorithm behavior patterns and generates predictions. This approach maintains detection capability while improving reliability by overcoming code obfuscation challenges through adaptive analysis.
4Reliability
If Controlled Folder Access whitelisting is used to prevent ransomware attacks, then protection capability is improved, but the system complexity increases and trusted applications become infection vectors
Solution Approach 1:
The patent applies self-service by implementing a deep learning engine that autonomously analyzes file features, generates decoy files, and detects ransomware attacks without requiring complex configuration or user intervention. The system self-adjusts to new ransomware variants by continuously learning from file patterns, thereby maintaining protection capability while reducing the operational complexity associated with traditional whitelisting methods.
Data Source
AI summary
A system and method for generating decoy files for protection against ransomware attacks is disclosed. The system includes a deep learning engine, wherein the deep learning engine is configured to extract a plurality of features from most recently used user files in a folder, convert the plurality of features to a vector format, estimate error of the plurality of features to a target vector, and generate decoy files if the error is less than a predefined threshold.