Ransomware Detection via File Extension Monitoring
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Solution Overview
Problem
Conventional ransomware detection techniques consume significant time and resources by analyzing file content, which is inefficient and costly, and often fail to detect ransomware attacks promptly.
Innovation Solution
The proposed method detects ransomware by analyzing file-related operations and multiple file extensions, identifying anomalous patterns without relying on content analysis, using a cloud-based application to monitor file operations and trigger alerts based on threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If file content analysis is used to detect ransomware, then detection accuracy is improved, but time consumption and computing resources increase substantially
Solution Approach 1:
The system performs preliminary monitoring of file operations and extension changes before ransomware encryption occurs. By tracking file extension modifications and operation patterns in advance, the system can detect ransomware behavior early in the attack process, avoiding the need for time-consuming post-encryption content analysis while maintaining high detection accuracy
Solution Approach 2:
The detection system is divided into multiple independent modules: file operation monitoring module, extension change tracking module, pattern analysis module, and alert generation module. Each module handles specific aspects of ransomware detection independently, allowing parallel processing that reduces overall detection time while maintaining comprehensive analysis accuracy
2Reliability
If file content analysis is used to detect ransomware, then detection capability is improved, but computing resources such as memory and processor cycles are consumed substantially
Solution Approach 1:
The system extracts and monitors only the critical external characteristics of files (extension changes, operation types, timing patterns) rather than analyzing the entire file content. This extraction approach focuses computational resources on the most indicative features of ransomware behavior, maintaining detection capability while dramatically reducing memory and processor requirements
Solution Approach 2:
The system uses lightweight metadata structures and temporary pattern buffers instead of loading and analyzing heavy file contents. These minimal data representations consume negligible memory and can be quickly created and discarded, enabling continuous monitoring without substantial computing resource consumption
3Reliability
If file content analysis is used to detect ransomware, then encryption detection is improved, but network bandwidth is consumed substantially
Solution Approach 1:
The system extracts encryption indicators from file metadata and operation logs rather than transmitting and analyzing actual file contents over the network. By monitoring extension changes and operation patterns locally, the system achieves encryption detection without consuming network bandwidth for data transmission
Data Source
AI summary
Techniques are described herein that are capable of detecting ransomware among files using information that is not included in content of the files. The files are determined based on file-related operation(s) being performed on each of the files during a session of a cloud-based application. A subset of the files is determined such that each file in the subset has multiple file extensions during the session. A value, which is based on a number of the files in the subset, is compared to a threshold. A determination is made whether the files include the ransomware based on whether the value is greater than or equal to the threshold. An alert, which indicates that the files include the ransomware, is selectively triggered based on detection of whether the files include the ransomware.