5G Core Network Attack Graph Analysis for Novel Vulnerability Detection
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Solution Overview
Problem
Existing security analysis tools for 5G networks fail to effectively discover and analyze complex attack vectors arising from vulnerabilities in software-defined and virtualized 5G core networks (5GCN), particularly due to the interaction of technologies like SDN and NFV, and lack detailed analyses of their impact on end-users.
Innovation Solution
A machine learning-based framework is employed to construct attack graphs from regular expressions representing system-level operations, perform linear searches to identify unexploited attack vectors, and use a trained machine learning module to predict new vulnerability exploits by linking unconnected nodes in the attack graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If existing security analysis tools are used to analyze 5G networks, then basic vulnerability detection is possible, but complex attack vectors arising from SDN and NFV interactions cannot be effectively discovered
Solution Approach 1:
The attack graph is segmented into multiple connected components representing different attack scenarios. The system divides the complex attack graph into manageable segments (e.g., SDN controller attacks, NFV attacks, combined attacks) that can be analyzed independently using machine learning models, then reassembled to provide comprehensive vulnerability detection across the entire 5G core network.
Solution Approach 2:
Machine learning models serve as intermediaries between the complex attack graph data and security analysts. The ML models process the attack graph, predict vulnerability exploits, and generate actionable security recommendations, reducing the cognitive load on analysts while enabling detection of complex attack vectors that would otherwise be overwhelming.
2Measurement precision
If manual security analysis is performed on 5GCN attack graphs, then detailed vulnerability analysis is possible, but manual effort and time consumption are excessive
Solution Approach 1:
The system performs self-service analysis by automatically processing attack graphs through machine learning models. The ML models independently predict vulnerability exploits, analyze attack vectors, and generate security recommendations without requiring manual intervention for each analysis step, thereby maintaining high precision while dramatically reducing time consumption.
Solution Approach 2:
Manual mechanical analysis of attack graphs is replaced with automated machine learning-based analysis. The system uses ML algorithms to process attack graph data, predict vulnerabilities, and generate recommendations, substituting human manual labor with automated computational processes that achieve comparable or superior precision with significantly reduced time investment.
3Productivity
If comprehensive attack graph analysis is performed, then all vulnerability exploits can be identified, but the computational resources and processing time required are excessive
Solution Approach 1:
Instead of performing exhaustive analysis of all possible attack vectors in the attack graph, the system uses machine learning models to prioritize and analyze the most critical vulnerability exploits. The ML models identify and focus computational resources on the most likely attack vectors based on trained patterns, achieving high productivity by avoiding unnecessary analysis of low-risk areas while maintaining comprehensive coverage of critical vulnerabilities.
Data Source
AI summary
According to various embodiments, a method for detecting security vulnerabilities in a fifth generation core network (5GCN) is disclosed. The method includes constructing an attack graph from a plurality of regular expressions. Each regular expression corresponds to a sequence of system level operations for a known 5GCN attack. The method further includes performing a linear search on the attack graph to determine unexploited 5GCN attack vectors where path in the attack graph that does not represent a known 5GCN attack vector represents an unexploited 5GCN attack vector. The method also includes applying a trained machine learning module to the attack graph to predict new 5GCN attacks. The trained machine learning module is configured to determine a feasibility of linking unconnected nodes in the attack graph to create a new branch representing a new 5GCN vulnerability exploit.