Machine-Learning Attack Path Training for Cloud-Native Analysis
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Solution Overview
Problem
Conventional attack-path analysis in cybersecurity is complex and laborious, struggling to keep up with the expanding attack surface of cloud-native applications, especially with the integration of generative AI-based attack techniques, and lacks efficient methods to prioritize and remediate vulnerabilities.
Innovation Solution
A machine-learning model is trained on known and auto-generated attack paths, inferring probabilistic attack paths and their probabilities, mapping physical to logical attack paths, and providing recommendations for prioritization and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional attack-path analysis methods are used, then analysis can be performed, but the process becomes complex and laborious, struggling to keep up with the expanding attack surface
Solution Approach 1:
The patent replaces manual, mechanical attack-path analysis processes with an automated machine-learning system. The ML model automatically analyzes application assets, topologies, and vulnerabilities to generate attack paths, eliminating the need for laborious manual analysis and enabling the system to keep pace with expanding attack surfaces.
Solution Approach 2:
The system enables self-service attack-path analysis by allowing the machine-learning model to autonomously process security data and generate attack path predictions without requiring manual intervention. The automated pipeline processes vulnerabilities, assets, and topologies to produce prioritized attack paths independently.
2Loss of information
If comprehensive attack-path analysis is performed on expanding attack surfaces, then visibility is improved, but engineering effort and resources increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training the machine-learning model on known attack paths and vulnerability patterns before actual analysis. This pre-training enables the model to quickly analyze new applications without requiring extensive manual engineering effort for each analysis, reducing time and resources while maintaining comprehensive visibility.
Solution Approach 2:
The system creates simplified representations (copies) of attack paths through the machine-learning model's predictions. Instead of manually analyzing every possible attack scenario, the model generates predicted attack paths that replicate the essential security risks, providing comprehensive visibility with reduced engineering effort.
3Reliability
If all vulnerabilities are analyzed without prioritization, then complete security assessment is achieved, but resource allocation for remediation becomes inefficient
Solution Approach 1:
The system applies local quality by providing differentiated treatment to different vulnerabilities based on their risk characteristics. The machine-learning model analyzes each vulnerability's context, assets, and topologies to assign priority levels, ensuring that remediation resources are allocated efficiently to the most critical issues while maintaining complete security assessment.
Solution Approach 2:
The system implements feedback by using the machine-learning model to continuously learn from analyzed attack paths and vulnerability patterns. The model's predictions about which attack paths are most likely provide feedback that guides resource allocation, enabling both complete assessment and efficient remediation prioritization.
Data Source
AI summary
In an embodiment, a method includes analyzing an application to determine its assets and topologies, executing a machine-learning model over the assets and topologies to predict logical attack paths and physical attack paths associated with each of the logical attack paths, wherein the physical attack paths associated with each of the logical attack paths map to that logical attack path, wherein each of the physical attack paths includes a respective set of physical assets of the application that can be used in a real-world attack, wherein each of the logical attack paths includes a sequence of logical steps of the real-world attack, and wherein the machine-learning model was trained based on training physical attack paths, training logical attack paths, and correlations between the training physical attack paths and training logical paths, and transmitting the predicted physical attack paths and the predicted logical attack paths to monitoring systems for display.