Attention-Based Cyberattack Path Prediction for Complex Networks
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Solution Overview
Problem
Existing attack graph methods require significant time, effort, and cost to generate and manage vulnerability databases, making it difficult to effectively respond to systematic cyberattacks due to the complexity and scale of network systems.
Innovation Solution
A machine-learning-based approach using a neural network model to predict cyberattack surfaces by generating and collecting virtual network topology and host asset information, extracting feature data, and training an intelligent attack path prediction model to quickly identify potential attack paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vulnerability scan tools are used to check security of individual hosts, then vulnerability information can be obtained, but the method is not sufficient to enable security managers to determine effective countermeasures when there is a large number of hosts or vulnerabilities
Solution Approach 1:
The patent segments the complex security management task into two parts: (1) automated attack path prediction using machine learning to identify critical attack sequences, and (2) security countermeasure recommendation based on predicted attack paths. This segmentation allows the system to handle large numbers of hosts and vulnerabilities by focusing on critical attack paths rather than analyzing all possible vulnerability combinations manually.
Solution Approach 2:
The patent introduces an attack graph as an intermediary representation that connects vulnerability information to security countermeasures. The attack graph visually represents attack paths and their probabilities, serving as a bridge between raw vulnerability data and actionable security recommendations, making it easier for security managers to understand and respond to threats.
2Loss of information
If attack graphs are generated to visually represent all attack paths, then detailed information for effective responses can be acquired, but a large-scale attack graph becomes difficult to manage and analyze due to the huge number of possible attack paths
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict and prioritize critical attack paths before generating the attack graph. Instead of generating all possible attack paths and then analyzing them, the system pre-identifies the most likely attack paths using trained models, thereby reducing the complexity of the attack graph while maintaining information about critical threats.
Solution Approach 2:
The patent changes parameters by introducing probability scores and risk weights to attack paths. Instead of treating all attack paths equally, the system uses parameter changes to prioritize high-risk paths, allowing security managers to focus on the most critical threats rather than being overwhelmed by the complete set of possible attack paths.
3Measurement precision
If attack graphs are generated using vulnerability information, then various attack paths can be accurately analyzed, but a lot of expense, time and effort is required to generate and manage the vulnerability database
Solution Approach 1:
The patent uses copying by training machine learning models on historical vulnerability data and attack patterns. Once trained, the model can predict attack paths for new vulnerability scenarios without requiring manual analysis of each individual vulnerability database, significantly reducing the time and effort required while maintaining accurate attack path analysis.
Solution Approach 2:
The system implements self-service by automatically updating and maintaining the vulnerability database through continuous learning from new vulnerability data and attack patterns. The machine learning model autonomously adapts to new threats, reducing the manual effort required to keep the vulnerability database current and accurate.
Data Source
AI summary
Disclosed herein are an apparatus and method for inferring a cyberattack path based on attention. The apparatus includes memory in which at least one program is recorded and a processor for executing the program. The program generates test data required for generating an intelligent attack graph and generates an attack graph based on an intelligent attack path prediction model.