Ransomware Prevention via File Access Segmentation
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Solution Overview
Problem
Current ransomware detection systems are resource-intensive and often fail to detect ransomware designed to ignore 'honey files,' leading to incomplete protection against cyber-attacks that irreversibly render files inaccessible.
Innovation Solution
A ransomware prevention system that monitors file access attempts, intercepts unauthorized access, and uses a combination of whitelisting, blacklisting, and heuristic analysis to classify file access events, preventing malicious operations and alerting administrators to potential ransomware threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current ransomware detection systems use honey files for detection, then ransomware detection capability is improved, but resource consumption increases and detection reliability decreases for advanced ransomware variants
Solution Approach 1:
The system segments file access monitoring into different categories: critical files requiring strict protection, optional files with conditional protection, and system files with managed access. This segmentation allows the system to allocate computational resources more efficiently by not uniformly monitoring all files with the same intensity, thereby reducing overall resource consumption while maintaining effective ransomware detection.
Solution Approach 2:
The patent applies different protection qualities to different file types and locations. Critical files receive strict access control and monitoring, while optional files receive conditional protection based on risk assessment. This local differentiation of protection quality enables the system to maintain high detection reliability for most ransomware variants without uniformly consuming maximum resources across all monitored objects.
2Reliability
If current ransomware detection systems rely on honey files, then detection of basic ransomware is improved, but detection of advanced ransomware that ignores honey files fails
Solution Approach 1:
The system implements a multi-functional detection approach that combines several mechanisms: honey files for basic detection, file access pattern analysis for behavioral detection, encryption operation monitoring for technical detection, and user activity correlation for contextual detection. This universal detection framework can adapt to various ransomware strategies including those that ignore traditional honey files, thereby improving detection accuracy across different ransomware variants.
Solution Approach 2:
The system continuously monitors file access patterns, encryption operations, and user behaviors, using this feedback to dynamically update detection models and risk assessments. When anomalous patterns are detected (such as multiple files being encrypted or specific access sequences), the system adjusts its detection parameters and alerting thresholds, enabling it to adapt to advanced ransomware variants that may ignore static honey file configurations.
3Reliability
If current ransomware prevention solutions block all unauthorized file access, then protection against ransomware is improved, but legitimate user operations are disrupted
Solution Approach 1:
The system dynamically adjusts access control policies based on real-time risk assessment, user identity, file importance, and operational context. Rather than applying static blocking rules, the system evaluates each access request against current conditions and adapts its response accordingly. This dynamic approach allows legitimate operations to proceed when risk is low while blocking ransomware activities when detected, maintaining ease of operation for users.
Solution Approach 2:
The system changes protection parameters such as access allowed/denied, monitoring intensity, and alerting thresholds based on multiple factors including user role, file criticality, access pattern history, and detected behavior. By adjusting these parameters dynamically rather than using fixed rules, the system maintains effective ransomware protection while minimizing disruption to legitimate user operations that fall within acceptable parameter ranges.
Data Source
AI summary
A computerized system and method to detect ransomware cyber-attacks is described. The approach entails analyzing the features associated with a file access event by a process operating on a computing device, to ascertain whether the process is associated with a ransomware cyber-attack.