Isma'ila, U.A. and Danyaro, K.U. and Muazu, A.A. and Maiwada, U.D. (2024) Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices. IEEE Access, 12. pp. 30941-30961. ISSN 21693536
Full text not available from this repository.Abstract
The novelty of Federated Learning (FL) has emerged as a promising alternative to centralized machine learning systems in the context of anomaly-based intrusion detection systems (AIDS) deployed on Internet of Things (IoT) devices. Unlike traditional centralized models, FL allows on-device model training and updates, reducing privacy concerns and issues such as single points of failure and high false alarm rates (FAR). This approach, termed 'Fed-AIDS,' offers a more secure and efficient solution. However, the development of Fed-AIDS models faces challenges related to limited training data and the diverse nature of IoT datasets. Additionally, FL's decentralized nature introduces weight divergence issues arising from non-Independently and Identically Distributed (non-IID) clients. To address these challenges and optimize Fed-AIDS modeling, interdisciplinary research efforts are vital. The primary objective of this study is to conduct an up-to-date review by adopting a Systematic Literature Review (SLR) approach to analyze existing studies of Fed-AIDS modeling procedures for IoT devices. Data from the published studies were retrieved from Scopus database, which covered major publishers such as IEEE, Elsevier and others. Specifically, our review conducted from the following Fed-AIDS perspectives: workflow and tools, training dataset, complexities of non-IID data in Fed-AIDS models, classification tasks, aggregation tasks, and model validation metrics. Based on the research findings, the study highlights a series of challenges and proposes potential solutions to stand in future research in Fed-AIDS modeling, aiming to advance the field of IoT device security. © 2013 IEEE.
Item Type: | Article |
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Additional Information: | cited By 0 |
Uncontrolled Keywords: | Classification (of information); Intrusion detection; Learning systems, Aggregation functions; Anomaly based intrusion detection systems; Anomaly-based intrusion detection; Centralised; Federated learning modeling; Independently and identically distributed; Internet of thing device; Learning models; Non-IDD data; System models, Internet of things |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 04 Jun 2024 14:19 |
Last Modified: | 04 Jun 2024 14:19 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/20143 |