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...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10857281/ |
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| author | Chirag Jitendra Chandnani Vedik Agarwal Shlok Chetan Kulkarni Aditya Aren D. Geraldine Bessie Amali Kathiravan Srinivasan |
| author_facet | Chirag Jitendra Chandnani Vedik Agarwal Shlok Chetan Kulkarni Aditya Aren D. Geraldine Bessie Amali Kathiravan Srinivasan |
| author_sort | Chirag Jitendra Chandnani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3234a318e83d420f91311205b1f936c7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3234a318e83d420f91311205b1f936c72025-02-06T00:00:38ZengIEEEIEEE Access2169-35362025-01-0113219922201010.1109/ACCESS.2025.353595210857281A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT NetworksChirag Jitendra Chandnani0https://orcid.org/0000-0001-8869-4374Vedik Agarwal1https://orcid.org/0009-0009-0028-879XShlok Chetan Kulkarni2https://orcid.org/0009-0002-3961-2875Aditya Aren3https://orcid.org/0009-0009-6113-5113D. Geraldine Bessie Amali4https://orcid.org/0000-0002-2939-4336Kathiravan Srinivasan5https://orcid.org/0000-0002-9352-0237School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaThe 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.https://ieeexplore.ieee.org/document/10857281/Hyperparameter optimizationfederated learningdeep learningintrusion detection systems |
| spellingShingle | Chirag Jitendra Chandnani Vedik Agarwal Shlok Chetan Kulkarni Aditya Aren D. Geraldine Bessie Amali Kathiravan Srinivasan A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks IEEE Access Hyperparameter optimization federated learning deep learning intrusion detection systems |
| title | A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks |
| title_full | A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks |
| title_fullStr | A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks |
| title_full_unstemmed | A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks |
| title_short | A Physics-Based Hyper Parameter Optimized Federated Multi-Layered Deep Learning Model for Intrusion Detection in IoT Networks |
| title_sort | physics based hyper parameter optimized federated multi layered deep learning model for intrusion detection in iot networks |
| topic | Hyperparameter optimization federated learning deep learning intrusion detection systems |
| url | https://ieeexplore.ieee.org/document/10857281/ |
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