Federated Malware Detection via Local Model Adaptation
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Solution Overview
Problem
Cyber attacks are becoming more costly and complex, with ransomware posing a significant threat to cyber resilience, as traditional methods struggle to detect abnormal snapshots and malware infections in increasingly complex network environments, leading to long and error-prone recovery times.
Innovation Solution
A dynamically federated data breach detection system that distributes trained supervised and unsupervised machine learning models to client devices, allowing for local adaptation and continuous updating, using transfer learning to enhance detection performance while preserving privacy and leveraging centralized resources for improved malware detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional centralized malware detection methods are used, then detection coverage can be maintained, but detection accuracy decreases and recovery time increases in complex network environments
Solution Approach 1:
The patent segments the centralized detection system into distributed local models deployed at client devices. Each local model independently analyzes data locally, enabling faster detection without waiting for centralized processing. This segmentation improves both detection accuracy through localized adaptation and reduces recovery time by eliminating centralized bottlenecks.
Solution Approach 2:
The patent introduces a federated learning dimension to the traditional centralized detection architecture. By adding the layer of distributed local models that learn from local data while sharing knowledge through federated averaging, the system achieves higher detection accuracy without increasing recovery time, as local models can operate independently while still benefiting from collective learning.
2Measurement precision
If local models are trained independently at each client device, then detection accuracy improves through local adaptation, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where local models send performance metrics and gradient information back to the server. The server aggregates this feedback and updates the global model, which is then redistributed to clients. This feedback loop enables automatic coordination of distributed models, improving detection accuracy through local adaptation while managing system complexity through automated feedback-driven synchronization.
Solution Approach 2:
The patent creates a universal federated learning framework that can be applied across diverse client devices and malware types. The same core architecture and communication protocols handle different detection scenarios, reducing system complexity despite the distributed nature of multiple local models. The framework serves multiple functions: local adaptation, knowledge sharing, and coordinated updates.
3Reliability
If more machine learning models are deployed for comprehensive detection, then detection coverage improves, but resource consumption increases
Solution Approach 1:
The patent merges multiple detection models into a unified federated learning system. Instead of running separate independent models, the system combines local adaptation capabilities with centralized knowledge sharing. This merging achieves comprehensive detection coverage by leveraging both local and global patterns while reducing resource consumption through shared model weights and coordinated updates that avoid redundant computations.
Data Source
AI summary
A processor distributes, from a server, a trained supervised machine learning (ML) model and supervised and unsupervised feature information to a plurality of client devices; at each client device, trains the supervised ML model using local data to generate a local supervised ML model, constructs a local unsupervised ML model using the unsupervised feature information, and deploys the local supervised and unsupervised ML models; determining when a detection performance difference between the local supervised and unsupervised ML models reaches a threshold; identifies a proposed change to the supervised or unsupervised feature information; deploys the proposed change on one client device; responsive to determining the proposed change improves the detection performance of that client device, communicates the proposed change to a sampled set of client devices; and responsive to determining the proposed change improves the detection performance of a majority of the sampled set, communicates the proposed change to the server.