SSFL-IDS

This repository contains the code for the paper:
Semi-Supervised Federated Learning Based Intrusion Detection Method for Internet of Things
In IEEE Internet of Things Journal, doi: 10.1109/JIOT.2022.3175918.

Overview of SSFL-IDS

Overview of proposed semi-supervised federated learning scheme for intrusion detection. Our CNN-based classifier is trained on the labeled local data with supervised training and on unlabeled open data with distillation training. Furthermore, we introduce multiple mechanisms to jointly improve the quality of global labels.

Schematic Illustration of Client Data

The schematic illustration of samples per class allocated to each client, where the x-axis indicates client IDs, the y-axis indicates class labels, and the size of scattered points indicates the number of labeled samples.

Dependency

torch=1.9.0
numpy=1.19.5
scikit-learn=0.24.2

Code

1. Unzip Dataset

cd data
unzip nba_iot_1000.zip

2. Train SSFL-IDS

python SSFL-IDS.py

Contact-Info

Ruijie Zhao
Email: ruijiezhao@sjtu.edu.cn

About

Link to our laboratory: SJTU-NSSL

Reference

R. Zhao, Y. Wang, Z. Xue, T. Ohtsuki, B. Adebisi, and G. Gui, ``Semi-Supervised Federated Learning Based Intrusion Detection Method for Internet of Things,'' IEEE Internet Things J., early access, doi: 10.1109/JIOT.2022.3175918.

@ARTICLE{SSFL_IDS,
  author    = {Zhao, Ruijie and Wang, Yijun and Xue, Zhi and Ohtsuki, Tomoaki and Adebisi, Bamidele and Gui, Guan},
  title     = {Semi-Supervised Federated Learning Based Intrusion Detection Method for Internet of Things},
  booktitle = {IEEE Internet of Things Journal},
  pages     = {1--14},
  doi={10.1109/JIOT.2022.3175918}},
  year      = {2022}}