Virtual Network Attack Simulation for AI Cybersecurity
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Solution Overview
Problem
Existing cybersecurity methods fail to effectively utilize data from network attacks and intrusions to train and deploy AI/ML algorithms to reduce or eliminate network vulnerabilities to cyber-attacks.
Innovation Solution
A system and method that monitors a target network using Open Systems Interconnection (OSI) layers, simulates cyber-attacks on a virtual network, and uses an AI engine to generate mitigation actions based on historical attack data, including deep neural networks for classification and risk scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI/ML algorithms are trained using historical attack data, then network security effectiveness is improved, but data processing complexity and system resource requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical attack data in a database before actual attacks occur. The AI/ML algorithms are trained in advance using this pre-prepared data, enabling rapid response to new attacks without the complexity of real-time training. This preliminary data preparation and model training resolves the contradiction by separating the complex training process from the operational security response.
Solution Approach 2:
The system creates a virtual copy of the network environment where historical attack data is simulated and replicated. This virtual replica allows AI/ML algorithms to be trained on past attack patterns without processing actual complex real-time network traffic. The copying of attack data into a controlled virtual environment simplifies the data processing complexity while maintaining security effectiveness.
2Reliability
If real-time network traffic analysis is performed, then attack detection capability is improved, but system performance and processing speed decrease
Solution Approach 1:
The system performs preliminary analysis by pre-training AI/ML models on historical attack data and storing attack signatures in databases. During actual network operation, the system only needs to match incoming traffic against these pre-computed models rather than performing complex real-time analysis, thereby maintaining high detection capability while improving processing speed.
Solution Approach 2:
The system replaces complex mechanical real-time analysis processes with AI/ML-based pattern recognition that uses pre-trained models. Instead of performing computationally intensive analysis on every packet, the system uses substituted AI inference mechanisms that leverage historical data patterns, significantly improving processing speed while maintaining detection effectiveness.
3Reliability
If comprehensive vulnerability scanning is conducted, then security coverage is improved, but time required for assessment increases
Solution Approach 1:
The system creates a virtual copy of the network topology and uses AI/ML algorithms to simulate and predict potential attack paths on this virtual replica. This allows comprehensive vulnerability assessment without actually scanning every possible vector in the real network, thereby maintaining extensive security coverage while significantly reducing assessment time through virtualization and predictive modeling.
Solution Approach 2:
The system uses feedback from historical attack data and simulated attack results to continuously improve its vulnerability assessment algorithms. By learning from past attack patterns and adjusting its predictive models, the system achieves comprehensive security coverage more efficiently, reducing the time required for assessment while maintaining thoroughness through iterative improvement.
Data Source
AI summary
A system and method for mitigating cyber-attacks against a target network comprising interconnected note that is implemented by Open Systems Interconnection (OSI) layers monitors the target network for detecting vulnerabilities across one or more OIS layers. a virtual network comprising a virtualized representation of the target network where the virtual network includes one or more virtual nodes that are annotated with identified vulnerabilities of one or more corresponding nods of the target network. A reference database can be configured to store records of known cyber-attacks and their corresponding mitigations where cyber-attacks on the virtual network are simulated based on records of known cyber-attacks and successful cyber-attacks. An AI engine can be configured to generate one or more mitigation actions based on simulation of the cyber-attacks before implementing the one or more mitigation actions to the target network.