Two-Tier Cybersecurity Method Using GAN and Federated Learning
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Solution Overview
Problem
Existing malware detection systems are susceptible to misclassifications and lack consideration for user privacy during data-sharing on networks, as they typically rely on conventional neural networks and centralized learning approaches that fail to ensure data security and privacy.
Innovation Solution
A two-tier cybersecurity method integrating a generative adversarial network (GAN) for malware detection with a privacy-preserving federated learning-based data-sharing model, where known files are converted into images for classification and user data is encrypted and stored securely, maintaining user privacy through a decentralized approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural networks are used for malware detection, then the detection process is simple, but misclassifications occur and user privacy is compromised
Solution Approach 1:
The system segments malware detection into two independent tiers: a GAN-based detection tier that analyzes file characteristics without accessing user data, and a federated learning tier that handles user data locally without centralizing it. This segmentation resolves the contradiction by improving reliability through specialized models while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a file conversion intermediary that transforms malware files into image format, enabling GAN-based detection. This intermediary layer allows conventional GAN models to effectively detect malware by converting it into a recognizable format, improving detection accuracy without requiring complex custom neural network architectures.
2Productivity
If centralized learning approaches are used, then data processing is efficient, but user privacy during data-sharing is compromised
Solution Approach 1:
The system segments the learning process into centralized GAN training for malware detection patterns and decentralized federated learning for user-specific adaptations. This segmentation allows efficient centralized processing for general malware detection while preserving user privacy through local data processing, resolving the contradiction between productivity and privacy protection.
Solution Approach 2:
The patent extracts user data from the centralized processing pipeline and processes it locally through federated learning clients. This extraction of sensitive user data from the central server while maintaining processing efficiency through distributed computation resolves the contradiction by enabling productive data processing without compromising user privacy.
3Reliability
If federated learning is implemented for privacy preservation, then user privacy is protected, but system complexity increases
Solution Approach 1:
The system segments privacy protection functions into standardized federated learning modules that can be independently implemented. By segmenting the complex privacy-preserving mechanisms into manageable client-side and server-side components, the system achieves strong privacy protection while keeping individual module complexities manageable.
4Measurement precision
If GANs are used for malware detection, then misclassifications are reduced, but computational requirements increase
Solution Approach 1:
The patent applies preliminary file conversion to transform malware into image format before GAN processing, and uses preliminary filtering through the two-tier architecture to route only relevant files through the computationally intensive GAN analysis. This preliminary preparation reduces the computational energy required by the GAN by optimizing its input and reducing unnecessary processing.
Data Source
AI summary
The two-tier cybersecurity method applies malware detection to a network. The malware detection is based on a generative adversarial network (GAN). Additionally, each user accesses the network through a privacy-preserving interface which applies a federated learning-based data-sharing model. Known malicious and known benign files are converted into corresponding images, and these corresponding images are used as input sample data for the generative adversarial network for generating a class vector for classifying and identifying malware. A malware detector uses the class vector output by the GAN for malware identification. Additionally, a privacy-preserving interface is established for each user of the network. The privacy-preserving interface is a federated learning-based data-sharing model.