Ransomware Detection Framework Using Threat Intelligence Graph
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Solution Overview
Problem
Existing ransomware detection mechanisms are typically reactive and lack intelligence and insight to be predictive and proactive, failing to detect potential ransomware attacks with high confidence without vast intelligence sources and lacking a centralized repository of ransomware attacks and techniques.
Innovation Solution
An intelligent, predictive framework using an NLP model to extract entities of ransomware threats from third-party threat intelligence sources, building a ransomware threat/attack repository using a graph database, and training a Neural Network-based Machine Learning model to predict ransomware threats by classifying scans from security tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive ransomware detection mechanisms are used, then implementation is simple, but detection capability and predictive power are insufficient
Solution Approach 1:
The system performs preliminary actions by building a centralized repository of ransomware attack patterns and techniques before actual attacks occur. Machine learning models are pre-trained on this repository to enable proactive detection and prediction of ransomware threats, transforming the system from reactive to predictive capability.
Solution Approach 2:
A centralized ransomware repository serves as an intermediary between third-party threat intelligence sources and the detection system. The repository stores structured attack patterns, techniques, and indicators of compromise, enabling the ML models to learn from consolidated threat intelligence without directly accessing multiple external sources.
2Measurement precision
If vast intelligence sources are accessed to improve detection confidence, then detection accuracy improves, but information processing complexity increases
Solution Approach 1:
The system extracts only the essential and relevant features from vast third-party threat intelligence sources into the centralized repository. By curating and structuring only critical attack patterns, techniques, and indicators, the system achieves high detection confidence without processing the full complexity of raw intelligence sources.
Solution Approach 2:
The system transforms unstructured threat intelligence data into structured parameters and features suitable for machine learning models. By changing the data representation from raw intelligence to standardized attack patterns and indicators, the system improves measurement precision while reducing processing complexity.
3Adaptability or versatility
If a centralized repository of ransomware attacks is built, then predictive capability improves, but data processing and storage requirements increase
Solution Approach 1:
The centralized repository is segmented into distinct components: attack patterns, techniques, indicators of compromise, and mitigation strategies. This segmentation allows the system to store and process different types of ransomware intelligence separately, improving predictive capability while managing data volume through organized structure.
4Measurement precision
If machine learning models are trained to predict ransomware variants, then detection accuracy improves, but training time and computational resources increase
Solution Approach 1:
The machine learning models are pre-trained on the centralized ransomware repository containing historical attack patterns and techniques. This preliminary training enables the models to quickly adapt to new ransomware variants with minimal additional training time, as the foundational knowledge is already embedded in the pre-trained models.
Data Source
AI summary
In one example method metadata about one or more entities of a ransomware threat and one or more relationships between the one or more entities of the ransomware threat is extracted from received cyber threat intelligence data by a threat decipher engine. The metadata about the one or more entities of the ransomware threat and the one or more relationships between the one or more entities of the ransomware threat is stored in a repository. A ransomware attack type included in received security sensor data is predicted by a threat prediction engine based on the metadata about the one or more entities of the ransomware threat and the one or more relationships between the one or more entities of the ransomware threat.