Cryptor Detection via Machine Learning File Analysis
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Solution Overview
Problem
Current methods for detecting cryptors that encode files in computer systems suffer from high levels of type I and type II errors, as they fail to effectively utilize the unique characteristics of sacrificial files created by malicious software, leading to incorrect classification of legitimate and harmful files.
Innovation Solution
A system and method utilizing trained machine learning models to identify suspect processes by analyzing file modifications, including entropy, metadata, and application information, to differentiate between cryptor and legitimate software activities, thereby reducing false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection methods are used to identify cryptors, then detection capability against known cryptors is improved, but the system fails to detect new forms of cryptors that use unique decompressors, leading to increased type II errors
Solution Approach 1:
The patent transitions from signature-based detection to behavior-based detection by changing the detection parameters from static file signatures to dynamic behavioral characteristics. The system monitors file modification patterns, entropy changes, and system call sequences to detect cryptor activity, enabling detection of new cryptor variants without requiring updated signatures.
Solution Approach 2:
The patent introduces dynamic monitoring of file system operations and process behavior to detect cryptors. Instead of relying on static signatures, the system continuously observes behavioral patterns such as rapid file modifications, entropy increases, and characteristic system call sequences, allowing adaptation to new cryptor forms through behavioral analysis.
2Adaptability or versatility
If behavioral detection methods are used to monitor cryptor activities, then flexibility in detecting various cryptor types is improved, but the system produces high levels of type I errors by misclassifying legitimate archiver applications as harmful
Solution Approach 1:
The patent applies local quality by analyzing specific behavioral characteristics at different stages of file operations. Instead of treating all file modifications uniformly, the system examines localized patterns such as entropy changes in specific file regions, metadata modification sequences, and system call patterns to distinguish cryptors from legitimate applications with higher precision.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring behavioral patterns and adjusting detection thresholds based on observed characteristics. The system learns from detected patterns to refine its classification criteria, reducing false positives while maintaining detection flexibility through adaptive behavioral analysis.
3Reliability
If back-up copying of affected files is implemented to counteract cryptor operations, then data protection is improved, but resource expenditure and system performance degradation increase
Solution Approach 1:
The patent implements preliminary action by detecting cryptor activities before they complete their encryption process. Through behavioral monitoring of file modification patterns and system calls, the system identifies and responds to cryptor operations in early stages, preventing full encryption and reducing the need for extensive back-up operations, thereby maintaining data protection while minimizing performance impact.
Data Source
AI summary
Disclosed herein are systems and methods for identifying a cryptor that encodes files of a computer system. An exemplary method comprises, identifying one or more files into which a data entry is performed by a suspect process; for each identified file, determining characteristics of the identified file, identifying classes of file modifications using a trained machine learning model and respective characteristics of the identified file, identifying a suspect process as being associated with the cryptor based on the identified classes of file modification of the file, and protecting the computer system from the cryptor.