Federated Cyber Threat Analysis With Attack Path Planning
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Solution Overview
Problem
Existing cybersecurity systems struggle to effectively analyze and predict cyber threats due to limited data processing capabilities and scope, often missing critical information when inspecting multiple sources together.
Innovation Solution
A system and method utilizing federated simulations, machine learning, and advanced tree graph searching techniques to gather and analyze network entity information, simulate network traffic, and generate models for risk evaluation, enabling automated task planning to minimize exposure and maximize resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional monitoring methods are used to detect threats by monitoring network and user behavior, then the system is easier to operate and implement, but the data processing capability and analysis scope are limited, causing critical information to be overlooked
Solution Approach 1:
The system segments the cybersecurity analysis into multiple specialized components: federated simulations for attack path generation, machine learning engines for behavior analysis, graph processing systems for topology analysis, and automated planning for response optimization. Each component handles a specific aspect of the complex analysis, enabling comprehensive information processing while maintaining manageable system architecture through modular division of functions.
2Measurement precision
If federated simulations and machine learning techniques are used to simulate network traffic and analyze behaviors, then the threat detection accuracy is improved, but the computing resources and processing time are significantly increased
Solution Approach 1:
The system performs preliminary actions by generating simulated attack paths and establishing baseline behavior profiles through federated simulations before actual threats occur. Machine learning models are pre-trained on simulated traffic patterns, enabling rapid detection during real operations without requiring intensive real-time computation for each threat assessment, thus reducing ongoing energy consumption while maintaining high detection accuracy.
Solution Approach 2:
The system applies partial action by focusing computational resources on analyzing only the most critical attack paths and behavior deviations identified through initial screening. Rather than exhaustively analyzing all possible network behaviors, the system prioritizes high-risk scenarios and anomalous patterns, achieving effective threat detection with reduced computing resource consumption by concentrating analysis on the most significant threats.
3Reliability
If comprehensive network entity information and topology data are gathered for analysis, then the cybersecurity risk evaluation is more accurate, but the data processing complexity and time requirements are increased
Solution Approach 1:
The system replaces traditional mechanical data processing methods with graph-based computational models and machine learning algorithms. Graph processing enables parallel analysis of network topology and entity relationships, while ML models automatically identify patterns and risks without requiring sequential manual analysis of each data point, significantly reducing processing time while maintaining comprehensive evaluation accuracy.
4Productivity
If automated task planning and response optimization are implemented using machine learning and tree graph searching, then the response efficiency is improved, but the system complexity and implementation difficulty are increased
Solution Approach 1:
The system introduces automated planning algorithms and tree graph searching mechanisms as intermediary layers between threat detection and response execution. These intermediaries automatically generate optimized response plans by evaluating multiple action sequences, selecting the most effective countermeasures without requiring manual intervention, thus improving response efficiency while managing implementation complexity through automated decision-making frameworks.
Data Source
AI summary
A system and method for cyber exploitation path analysis and response using federated networks to minimize network exposure and maximize network resilience, with the ability to simulate complex and large scale network traffic through the use of federated training networks, by gathering network entity information, establishing baseline behaviors for each entity, and monitoring each entity for behavioral anomalies that might indicate cybersecurity concerns. Further, the system and method involve incorporating network topology information into the analysis by generating a model of the network, annotating the model with risk and criticality information for each entity in the model and with a vulnerability level between entities, and using the model to evaluate cybersecurity risks to the network. Lastly, network attack path analysis and automated task planning for minimizing network exposure and maximizing resiliency is performed with machine learning, generative adversarial networks, hierarchical task networks, and Monte Carlo search trees.