A review of Federated Learning in Intrusion Detection Systems for IoT

Belenguer, Aitor; Navaridas, Javier; Pascual, Jose A.

doi:10.48550/arxiv.2204.12443

Cited by 6 publications

(6 citation statements)

References 71 publications

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“…Jiehan et al [28] discuss the future trends on accelerating industrial IoT privacy preservation and model optimization based on FL. Additionally, Aitor's et al [29] assesses the absence of consistency in model evaluation, provides the development of a road map for quality standards to deal with advancement of IDS models. Meanwhile, Bhabendu K. et al [30] investigate the applications of ML, AI, and Blockchain in resolving these security challenges in IoT.…”

Section: A Motivation and Related Workmentioning

confidence: 99%

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Isma’ila,

Danyaro,

Muazu

et al. 2024

IEEE Access

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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 like 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 like 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.

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Section: A Motivation and Related Workmentioning

confidence: 99%

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Isma’ila,

Danyaro,

Muazu

et al. 2024

IEEE Access

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“…This work is aligned with Sarhan et al [ 37 ] where a hierarchical federated learning framework has been designed to operate using blockchain. Federated learning in IoT IDS has been extensively studied and benchmarked in the literature such as in [ 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Related Workmentioning

confidence: 99%

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Hajj

Azar

Abdo

et al. 2023

Sensors

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With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system’s performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.

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“…Many new FLSs have emerged since the creation of FL in 2016. There are a general taxonomy describing the difference of FLS is was presented in [19] and also replicated in [27]. Even though their taxonomy was very helpful for many researchers, it had several limitations that need to be addressed.…”

Section: Federated Learning Systems Taxonomymentioning

confidence: 99%

“…This review also outlines potential future directions for FL in IDS. Meanwhile, the authors of [27] focus on the current scientific progress of FL applications in attack detection problems for IoT and explore these applications. The extensive review presented in [50] draws from an analysis of 39 research papers published from 2018 to March 2022, with a specific focus on the IoT.…”

Section: Related Surveysmentioning

confidence: 99%

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Alsamiri,

Alsubhi

2023

Future Internet

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In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

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A review of Federated Learning in Intrusion Detection Systems for IoT

Cited by 6 publications

References 71 publications

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

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