A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks

Author:

Rashid Md Mamunur1ORCID,Khan Shahriar Usman2,Eusufzai Fariha3,Redwan Md. Azharuddin3,Sabuj Saifur Rahman3ORCID,Elsharief Mahmoud4ORCID

Affiliation:

1. Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea

2. Institute of Information Technology, Jahangirnagar University, Dhaka 1342, Bangladesh

3. Department of Electrical and Electronic Engineering, Brac University, Dhaka 1212, Bangladesh

4. Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea

Abstract

The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.

Publisher

MDPI AG

Subject

Pharmacology (medical),Complementary and alternative medicine,Pharmaceutical Science

Reference36 articles.

1. Efficient and flexible management for industrial Internet of Things: A federated learning approach;Guo;Comput. Netw.,2021

2. Bag, S. (2022, August 12). Federated Learning—A Beginners Guide. Available online: https://www.analyticsvidhya.com/blog/2021/05/federated-learning-a-beginners-guide/.

3. Federated Learning for 6G: Applications, Challenges, and Opportunities;Yang;Engineering,2022

4. Machine Learning Approaches to IoT Security: A Systematic Literature Review;Ahmad;Internet Things,2021

5. Intrusion detection system: A comprehensive review;Liao;J. Netw. Comput. Appl.,2013

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