Three-Sided Game Theory Cyber Security Honeypot System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network security methods face challenges such as high false positives, difficulty in detecting complex attacks, inability to adapt to new attack types, passive attack identification, and ineffective mitigation of network threats, particularly in predicting future attacks and handling multiple simultaneous attacks.
Innovation Solution
A three-sided game-theoretic analysis system using a honeypot as a supportive side, combined with passive and active network sensors, employs a geometric solution based on a three-dimensional action curve and surface to determine Nash equilibriums, providing adaptive and efficient defense strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If passive network sensors are used for attack detection, then the system complexity is reduced, but the detection capability for complex attacks deteriorates
Solution Approach 1:
The patent combines passive network sensors, active network sensors, and game-theoretic analysis into a unified security system. The passive sensors provide baseline detection while active sensors engage attackers, and game theory optimizes their coordinated response, achieving superior complex attack detection without excessive complexity increase.
Solution Approach 2:
Game-theoretic analysis serves as an intermediary layer that processes information from both passive and active sensors. It models the strategic interactions between defenders and attackers, synthesizing sensor data into optimized detection and mitigation strategies that overcome the limitations of individual sensor types.
2Ease of manufacture
If traditional attack identification methods are used, then the implementation is simple, but the adaptability to new attack types deteriorates
Solution Approach 1:
The patent implements dynamic adaptation through game-theoretic modeling that continuously updates defender strategies based on observed attacker behavior. The system learns from actual attack patterns and adjusts detection and mitigation strategies in real-time, maintaining simplicity while achieving high adaptability to emerging threats.
Solution Approach 2:
The system incorporates feedback loops where attack detection results and attacker responses are fed back into the game-theoretic model. This enables continuous refinement of detection strategies, allowing the system to adapt to new attack types while maintaining implementation simplicity through automated learning processes.
3Device complexity
If ad hoc mitigation recommendations are provided, then the system complexity is reduced, but the effectiveness of threat mitigation deteriorates
Solution Approach 1:
The game-theoretic model performs preliminary analysis of potential attack vectors and predicts optimal mitigation strategies before attacks occur. By pre-computing response strategies based on modeled attacker behavior, the system achieves high mitigation effectiveness without requiring complex real-time decision-making during actual attacks.
Solution Approach 2:
The system dynamically adjusts mitigation parameters based on game-theoretic optimization, changing detection thresholds, alert priorities, and response strategies according to the current security situation. This enables effective adaptation to evolving threats while maintaining manageable system complexity through parameterized control.
Data Source
AI summary
Methods and systems for providing cyber security, wherein a computer with network access incorporates game theory and utilizes a honeypot to enhance game-theoretic developments over active and passive sensors. To numerically solve the uniquely three-sided game modeled cyber security problem, using a geometric solution based on three-dimensional (3D) action surface and action curve. The methods and systems determine whether the game problem has one Nash equilibrium, multiple Nash equilibriums, or no Nash equilibrium; checks whether the equilibrium is a mixed or pure Nash; and timely computes Nash equilibriums; and follows a fictitious play concept. The solution is adaptive and can be applied for any partially observed cyber security system.