A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks
The Internet of Things (IoT) is reshaping our lives with its omnipresence. The sudden uptick in the ubiquitous nature of IoT devices ranging from fitness watches to aircraft has led to a surge of cyber-attacks. Artificial Intelligence powered Intrusion Detection Systems (IDS) are being used recently...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10857281/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The Internet of Things (IoT) is reshaping our lives with its omnipresence. The sudden uptick in the ubiquitous nature of IoT devices ranging from fitness watches to aircraft has led to a surge of cyber-attacks. Artificial Intelligence powered Intrusion Detection Systems (IDS) are being used recently to combat this increasing surge of attacks in the IoT environment. However, existing solutions lack optimization for training in distributed decentralized environments. A popular solution for training a model in a decentralized environment is Federated Learning. Multiple client models collaboratively train a global model while keeping the individual client’s data decentralized and private. This, however, suffers from poor generalization of the individual client data. This work proposes a new Federated Multi-Layered Deep-Learning (Fed-MLDL) model that employs physics-based hyperparameter optimization (HPO) technique FedRIME in a distributed federated learning environment for intrusion detection on the CICIoT23, CICIoT22, ToN_IoT, Edge_IIoT and, IoT-23 datasets. FedRIME ensures good generalization for all clients’ data by finetuning the model’s hyperparameters according to each client. The experimental results indicate that the Fed-MLDL with Fed-RIME optimization exhibits the highest accuracy for independent and identically distributed datasets with the scores being 99.2% with CICIoT23, 98.1% with CICIoT22, 98.2% with ToN_IoT, 98.5% with Edge_IIoTset and, 98.6% with IoT-23 dataset respectively. Further, the proposed Fed-MLDL with Fed-RIME optimization has demonstrated a significant improvement in the speed of convergence, stability, and client specific customization in federated learning. The study provides a comprehensive comparison with the most recent physics based HPO techniques. This study observes that coupling a Deep-Learning model with HPO techniques results in a much faster convergence requiring only 10-15 communication rounds. The proposed Fed-MLDL with Fed-RIME optimization outperforms existing state-of-the-art models on the CIC-IoT23 dataset. |
|---|---|
| ISSN: | 2169-3536 |