Ransomware Detection via User Behavior and System Status Monitoring
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Solution Overview
Problem
Existing antivirus technologies face challenges in detecting unknown ransomware programs that obstruct user interaction with the operating system, as they often cannot differentiate between malicious and legitimate software behaviors, leading to potential blockage of user access to antivirus tools.
Innovation Solution
A method and system that monitor user behavior patterns and operating system status patterns to detect ransomware by comparing current interactions against reference sets, using a user input monitoring module, input data analysis module, and decision module to identify and remediate ransomware presence, allowing for timely intervention without external utilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If behavior analysis and code emulation are used to detect malicious software, then the ability to differentiate safe programs from malicious ones is improved, but false positives occur when legitimate software performs actions similar to malicious programs (e.g., full-screen applications)
Solution Approach 1:
The system dynamically adjusts detection parameters and thresholds based on real-time analysis of user behavior patterns and system state changes. Instead of using static detection rules, the system adapts its sensitivity and classification criteria during operation, allowing it to distinguish between legitimate full-screen applications and ransomware by observing dynamic behavioral characteristics such as interaction frequency, system resource usage patterns, and user response times.
2Reliability
If traditional signature detection and heuristic analysis are used, then known malicious programs can be detected, but unknown ransomware programs that obstruct user interaction cannot be quickly detected
Solution Approach 1:
The system performs preliminary monitoring and collection of baseline user behavior patterns and system state information before ransomware infection occurs. By establishing normal operational profiles in advance, the system can quickly compare current system state against these pre-established baselines, enabling rapid detection of deviations caused by unknown ransomware without requiring time-consuming analysis or signature updates.
Solution Approach 2:
The system implements continuous feedback loops where detection results, whether from signature matching or behavioral analysis, are fed back into the system to refine future detection parameters. This feedback mechanism allows the system to learn from both confirmed malware detections and false positives, improving its ability to quickly identify unknown ransomware variants by recognizing emerging patterns in real-time system behavior.
3Productivity
If ransomware detection is delayed, then user access to antivirus tools is blocked, but quick detection requires complex monitoring of user behavior and system state
Solution Approach 1:
The system employs multi-functional monitoring components that simultaneously perform multiple tasks: collecting user behavior data, analyzing system state changes, comparing against known signatures, and detecting anomalies all through integrated modules. This universal approach allows quick detection of ransomware without requiring separate complex systems for each function, as the same infrastructure serves multiple detection purposes efficiently.
Data Source
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AI summary
System and method for detecting ransomware. A current user behavior pattern is monitored based on user input via a user input device. The user behavior is compared against a reference set of behavior patterns associated with user frustration with non-responsiveness of the user interface module. A current status pattern of the operating system is also monitored. The current status pattern is compared against a reference set of operating system status patterns associated with predefined ransomware behavior. In response to indicia of current user frustration with non-responsiveness of the user interface, and further in response to indicia of the current status pattern having a correlation to the predefined ransomware behavior, an indication of a positive detection of ransomware executing on the computer system is provided.