Cyber-Physical System Vulnerability Assessment via Generative Adversarial Networks
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Solution Overview
Problem
Industrial and infrastructure systems, particularly SCADA systems, face significant vulnerabilities due to their complex networks and potential internet access, making them difficult to secure against cyber threats.
Innovation Solution
A computer-implemented method involving the training of a machine learning model using a generative model and a discriminator, where a random parameter vector and attack dataset are generated, and the model learns to differentiate between random and generated attack datasets, ultimately improving cybersecurity by simulating and assessing attacks on physical plants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SCADA systems are connected to internet and covered large areas to improve monitoring and operation capabilities, then system functionality and coverage are improved, but security vulnerability and difficulty to secure increase
Solution Approach 1:
The patent applies preliminary action by proactively generating and training attack datasets before actual cyber attacks occur. The system pre-generates diverse attack scenarios (sensor attacks, actuator attacks, data injection) and trains machine learning models to recognize and prevent these attacks, thereby preparing the SCADA system in advance against potential threats while maintaining its expanded functionality and coverage
Solution Approach 2:
The patent converts the harmful nature of cyber attacks into beneficial security training data. By systematically generating attack datasets and using them to train discriminators and generators, the system transforms potential harmful inputs into useful learning resources that enhance the system's ability to detect and prevent future attacks, thereby improving security without compromising operational capabilities
2Measurement precision
If machine learning models are trained using generated attack datasets to improve attack simulation accuracy, then vulnerability assessment quality is improved, but computational complexity and training time increase
Solution Approach 1:
The patent uses copying by creating synthetic attack datasets that replicate real attack patterns. The generator model creates simplified representations of complex attack scenarios, allowing the system to train on representative examples rather than requiring exhaustive real attack data, thereby reducing computational complexity while maintaining assessment quality
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the complexity and scope of attack simulations based on training progress and system needs. The generator model can adapt the granularity, duration, and intensity of simulated attacks, allowing efficient training on diverse scenarios without proportionally increasing computational resources required
Data Source
AI summary
Methods for securing cyber-physical systems include attack generation systems, methods, and devices configured to assess vulnerabilities of cyber-physical systems include an example method of training an attack generative model using an attack policy to generate an attack dataset, training discriminators using a random attack dataset and generated attack dataset and training a generator using the trained discriminators.