Federated Intrusion Detection Using Fog Nodes for IoT Constraints

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

IoT devices often lack the processing power and memory capacity to train deep learning models, and existing federated learning architectures for intrusion detection are based on simulations rather than actual devices, leading to vulnerabilities due to lengthy model deployment processes and high communication costs.

Innovation Solution

Implement a federated learning system with a central server, edge nodes, and fog nodes to distribute and update intrusion detection systems, utilizing generative adversarial networks and lossless compression to enhance model training and reduce communication overhead, with fog nodes handling local model training for resource-constrained edge devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If federated learning is implemented on resource-constrained IoT devices, then intrusion detection capability is improved, but device processing power and memory capacity are insufficient

Engineering Contradiction:

Improveintrusion detection capabilityVSAvoidprocessing power and memory capacity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces fog nodes as intermediary devices between IoT devices and the central server. These fog nodes possess greater computational resources and are configured to train local security models on behalf of resource-constrained IoT devices, thereby enabling intrusion detection capabilities that would otherwise be unavailable on the IoT devices themselves.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments the federated learning architecture into multiple layers: IoT devices for data collection, fog nodes for local model training, and a central server for global model coordination. This segmentation allows each component to operate within its resource constraints while contributing to the overall intrusion detection capability.

Inventive Principle:

Principle #1Segmentation

2Reliability

If traditional model deployment processes are used, then intrusion detection can be implemented, but deployment time is lengthy and communication costs are high

Engineering Contradiction:

Improveintrusion detection effectivenessVSAvoidmodel deployment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary model training at fog nodes before deployment to IoT devices. By pre-training local security models using captured security data during live operation at the fog layer, the system reduces the time required for model deployment and updates at the IoT device level.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The fog nodes continuously train local security models using security data captured during live operation, ensuring that intrusion detection capabilities are continuously updated without interrupting the normal operation of IoT devices. This continuous training process eliminates lengthy deployment pauses.

Inventive Principle:

Principle #20Continuity of useful action

3Reliability

If security models are updated frequently to maintain detection accuracy, then intrusion detection reliability is improved, but communication overhead and costs increase

Engineering Contradiction:

Improvedetection accuracyVSAvoidcommunication overhead and costs

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent extracts the computationally intensive model training process from the central cloud and relocates it to the fog layer. This extraction allows local security models to be trained and updated at fog nodes using local resources, significantly reducing the communication overhead required for model updates while maintaining detection accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12603898B2Systems and methods for intrusion detection using federated learning

Publication Date: 2026.04.14 NORTH CAROLINA AGRICULTURAL AND TECHNICAL STATE UNIVERSITY

AI summary

Methods and systems for intrusion detection using federated learning. In some examples, a system includes a central server configured for generating an initial security model and distributing the initial security model to edge nodes. Each edge node is configured for executing an intrusion detection system. The system includes a first subset of edge nodes each of which is configured for training a respective local security model using captured security data during live operation. The system includes a second subset of edge nodes each of which is not configured for training a local security model. The system includes fog nodes, each fog node being on a communications path between at least one edge node and the central server. At least a first fog node is configured for training a respective local security model for one or more of the edge nodes from the second subset of edge nodes.