Cryptographic API Call Graphs for Ransomware Detection
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Solution Overview
Problem
Ransomware attacks have evolved to be highly sophisticated, exploiting vulnerabilities within applications to encrypt critical files, making rapid detection and response challenging due to their ability to obfuscate and dynamically load cryptographic code.
Innovation Solution
An agent intercepts cryptographic API calls to construct a dynamic execution graph, calculating cryptographic entanglement metrics that characterize structural properties, using machine learning models to identify ransomware through behavioral patterns, and initiating remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signature-based detection methods are used, then known ransomware variants can be identified, but sophisticated ransomware with obfuscated and dynamically loaded cryptographic code can evade detection
Solution Approach 1:
The system dynamically constructs execution graphs that adapt to the runtime behavior of monitored processes. Instead of relying on static signatures, the system builds graphs that reflect actual cryptographic API call patterns during execution, allowing it to detect previously unseen ransomware variants by comparing their dynamic behavior against established patterns of legitimate cryptographic usage.
Solution Approach 2:
The patent transitions from analyzing individual cryptographic API calls in isolation to analyzing the structural relationships and entanglement patterns across multiple calls in a graph dimension. By computing metrics such as graph density, betweenness centrality, and clustering coefficients, the system detects ransomware based on the topological structure of its cryptographic behavior rather than specific call signatures, adding a structural dimension to the detection approach.
2Measurement precision
If comprehensive monitoring of all cryptographic API calls is implemented, then detection accuracy improves, but system performance and processing overhead increase
Solution Approach 1:
The system extracts only the essential cryptographic API call information needed for graph construction, filtering out unnecessary data. By focusing specifically on cryptographic function calls and their relationships rather than monitoring all process activities, the system maintains high detection precision while reducing processing overhead and preserving system performance.
Solution Approach 2:
The system applies partial monitoring by focusing specifically on cryptographic API calls rather than all process behaviors. This selective approach captures sufficient information to detect ransomware through cryptographic pattern analysis while avoiding the performance penalty of comprehensive full-process monitoring, achieving an optimal balance between detection capability and system impact.
3Reliability
If complex graph analysis and multiple metrics are computed, then ransomware detection capability improves, but computational complexity and processing time increase
Solution Approach 1:
The system pre-computes and stores baseline cryptographic execution patterns from legitimate processes during a training phase. When monitoring actual processes, it compares their execution graphs against these pre-established baselines using the entanglement metrics, significantly reducing the computational complexity required for real-time detection while maintaining high reliability in distinguishing malicious from benign processes.
Data Source
AI summary
An agent executing on a monitored computing device intercepts runtime execution data associated with a process by utilizing hooks into cryptographic application programming interfaces (APIs). The agent constructs a dynamic execution graph, where each node represents an intercepted cryptographic API call and each edge reflects an inferred relationship between nodes derived from the runtime execution data. Cryptographic entanglement metrics are computed based on the dynamic execution graph, characterizing structural properties of cryptographic behavior within the process. When these computed metrics indicate that the process is part of a ransomware attack, one or more remediation actions are initiated to thwart the advancement of the attack.