Federated Learning Intrusion Detection Model Based on Multi-channel Fusion Convolution
With the integration of next-generation information technologies across various industries,the digital transformation of economic and social sectors has become imperative.In recent years,with the advent of the Agriculture 4.0 era,the agricultural sector faces new opportunities and challenges.While the large-scale deployment of the Agricultural Internet of Things brings many conveniences,network security issues have also emerged.Traditional intrusion detection devices are inadequate in effectively handling various unknown attacks.The phenomena of data silos and the need for data privacy protection exacerbate these challenges.To address this,the paper innovatively proposes a federated learning intrusion detection model for agricultural IoT based on multi-channel fusion convolution.The model aims to resolve the contradictions between data isolation and data privacy protection while enhancing the model's accuracy.The data processing module of the model utilizes generative adversarial networks to augment undersampled data.The data analysis module adopts a horizontal federated learning mechanism.The server-side employs a federated averaging algorithm,and the client-side utilizes a one-dimensional multi-channel fusion convolutional network.This setup processes the same data segment with convolutional kernels of various sizes to capture feature information at different scales.These features are then fused together,effectively preserving key traffic characteristics.The experimental results show that the model can achieve an accuracy of 98%and converge quickly in the initial stage on the CIC-IDS2017 dataset.After 10 rounds of training,it tends to stabilize,achieving an accuracy of 99.72%and F1 score.
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