Federated GAN Training for Privacy-Preserving Cyberattack Detection
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Solution Overview
Problem
Traditional cybersecurity methods are ineffective against sophisticated cyber-attacks and require significant manual effort, while deep neural networks (DNNs) struggle with data privacy and sharing, necessitating a more robust and efficient approach.
Innovation Solution
A system and method leveraging federated learning and generative adversarial networks (GANs) to train DNNs on decentralized data, using adversarial training to enhance robustness, with a generator network generating samples difficult to distinguish from real samples, and a discriminator network evaluating them, while preserving data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity rules and policies are used, then manual effort is required to maintain security, but the system becomes ineffective against sophisticated attacks
Solution Approach 1:
The patent replaces manual cybersecurity operations with deep learning-based automated systems. Neural networks are trained to automatically detect and respond to cyber threats, substituting human analysts with machine learning models that can process network traffic patterns, identify anomalies, and respond to attacks without manual intervention, thereby improving both effectiveness against sophisticated attacks and reducing manual effort
Solution Approach 2:
The system enables self-service cybersecurity through autonomous threat detection and response capabilities. The deep learning models continuously monitor network traffic, automatically identify malicious patterns, and trigger defensive actions without requiring human operators, allowing the system to protect itself and adapt to new threats autonomously
2Reliability
If deep learning approaches are used to detect cyber-attacks, then detection accuracy improves, but data privacy concerns prevent data collection and sharing
Solution Approach 1:
The patent segments the training process into decentralized federated learning nodes distributed across multiple organizations. Each node trains local models on its own private data without sharing the actual data, only exchanging model updates. This segmentation allows detection accuracy to improve through collective learning while preserving data privacy by keeping sensitive information localized
Solution Approach 2:
The system introduces federated learning as an intermediary mechanism that enables collaboration without direct data exchange. The federated learning platform coordinates model training across distributed nodes, allowing organizations to benefit from collective intelligence while maintaining data sovereignty and privacy through cryptographic protocols and secure aggregation techniques
3Loss of information
If federated learning is used to train DNNs on decentralized data, then data privacy is preserved, but model training complexity increases
Solution Approach 1:
The patent develops a universal federated learning framework that can be applied across different organizations, network types, and threat scenarios. The standardized protocol and modular architecture allow the same federated learning infrastructure to serve multiple purposes and environments, reducing the perceived complexity by providing a repeatable, scalable solution rather than custom implementations for each case
4Reliability
If GANs are used to generate diverse training samples, then model robustness improves, but computational resources required increase
Solution Approach 1:
The patent applies partial action by using GANs selectively to generate only the most critical adversarial examples needed for robust training, rather than generating all possible variations. The system focuses computational resources on creating targeted adversarial samples that address specific vulnerability patterns, achieving improved model robustness with reduced computational overhead compared to exhaustive generation approaches
Data Source
AI summary
The system comprises a FL-based generative adversarial network (GAN) for generating adversarial examples, wherein the GAN includes a generator for generating the adversarial examples and a discriminator for distinguishing the adversarial examples from the original data, wherein the FL network includes multiple clients, each having a local dataset and a local DNN model, and a central server for coordinating the training process; a DNN for classifying data, where the DNN is trained using the generated adversarial examples, wherein the training process includes exchanging the model updates between the client's server and the central server; an evaluation module for measuring the adversarial accuracy and adversarial robustness of the DNN using appropriate metrics, including the adversarial accuracy, the adversarial loss, and the robustness to perturbations; and an adjustment module for adjusting the architecture or parameters of the DNN based on the evaluation results to improve its adversarial robustness.