System Call Pattern Detection for Ransomware Encryption
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Solution Overview
Problem
Existing systems face challenges in detecting and mitigating malware, particularly ransomware, which can infect production systems and backups, leading to data unavailability and costly recovery processes, often involving law enforcement and insurance companies.
Innovation Solution
A forensic engine is used to detect malware by analyzing system call patterns during the encryption phase, generating scores based on these patterns, and triggering protective actions when thresholds are exceeded, integrating inline and offline analysis to stop or mitigate attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection methods are used, then detection capability is limited, but system performance and resource usage remain acceptable
Solution Approach 1:
The patent replaces traditional signature-based malware detection (mechanical/systematic approach) with machine learning-based detection that analyzes system call patterns. The forensic engine uses trained machine learning models to automatically identify ransomware behavior by comparing observed system call sequences against learned patterns of malicious encryption activities, substituting rule-based mechanics with intelligent pattern recognition.
Solution Approach 2:
The patent changes the detection parameters from static signature matching to dynamic system call pattern analysis. By monitoring sequences of system calls (e.g., file access, encryption function calls, registry modifications) and their temporal patterns, the system adapts to detect previously unknown ransomware variants that exhibit characteristic behavioral parameter sequences during encryption operations.
2Loss of time
If real-time malware detection is implemented, then response time is reduced, but computational resources and processing overhead increase
Solution Approach 1:
The patent applies partial monitoring by focusing detection resources on specific high-value targets during the encryption phase. Rather than continuously analyzing all system calls, the forensic engine selectively monitors system calls related to file encryption operations, registry changes, and backup system interactions - the critical phases where ransomware exhibits distinctive patterns. This partial action approach reduces computational overhead while maintaining effective real-time detection.
Solution Approach 2:
The system performs preliminary machine learning model training and system call pattern baseline establishment during offline phases before production use. By pre-processing and pre-training detection models offline, the real-time detection burden is significantly reduced, allowing rapid online inference with minimal computational resources while maintaining high detection accuracy during critical encryption phases.
3Measurement precision
If comprehensive system monitoring is performed, then detection accuracy improves, but system performance and operational speed decrease
Solution Approach 1:
The patent segments the detection process into distinct phases: offline training phase, online detection phase, and response phase. During online operation, further segmentation occurs by monitoring only critical system call categories (file operations, encryption functions, registry modifications) rather than all system calls. This multi-level segmentation maintains comprehensive monitoring capability while preserving system operational speed by distributing analysis across time and call categories.
Solution Approach 2:
The forensic engine acts as an intermediary layer between the monitored system and the analysis pipeline. It captures system call data through hooks or telemetry, buffers and pre-processes the data stream, and passes filtered relevant information to the machine learning model for analysis. This intermediary approach decouples comprehensive monitoring from intensive analysis, maintaining system performance while enabling accurate detection through staged processing.
Data Source
AI summary
System calls performed by processes in a computing system are monitored and scored. The score is maintained over a time window. When the score exceeds a threshold score for a process in the time window, the process is determined to be a malware process and a protective operation is performed.