Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag, Mohamed Amine; Friha, Othmane; Μαγλαράς, Λέανδρος; Janicke, Helge; Shu, Lei

doi:10.1109/access.2021.3118642

Cited by 154 publications

(50 citation statements)

References 157 publications

(124 reference statements)

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“…The proposed model is validated based on the three types of GRU models as shown in Table 2. As mentioned earlier, numerous statistical parameters have been calculated in comparison to state-of-the-art studies of Logistic Regression (LR [18]), Recurrent Neural Network (RNN [24]), and Deep Neural Network (DNN [25]) to determine the performance enhancement of the proposed approach. The window size is considered for different scenarios using linear-quadratic-linear functions for better assessment of the proposed model.…”

Section: Resultsmentioning

confidence: 99%

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Federated Learning-Inspired Technique for Attack Classification in IoT Networks

et al. 2022

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More than 10-billion physical items are being linked to the internet to conduct activities more independently and with less human involvement owing to the Internet of Things (IoT) technology. IoT networks are considered a source of identifiable data for vicious attackers to carry out criminal actions using automated processes. Machine learning (ML)-assisted methods for IoT security have gained much attention in recent years. However, the ML-training procedure incorporates large data which is transferable to the central server since data are created continually by IoT devices at the edge. In other words, conventional ML relies on a single server to store all of its data, which makes it a less desirable option for domains concerned about user privacy. The Federated Learning (FL)-based anomaly detection technique, which utilizes decentralized on-device data to identify IoT network intrusions, represents the proposed solution to the aforementioned problem. By exchanging updated weights with the centralized FL-server, the data are kept on local IoT devices while federating training cycles over GRUs (Gated Recurrent Units) models. The ensemble module of the technique assesses updates from several sources for improving the accuracy of the global ML technique. Experiments have shown that the proposed method surpasses the state-of-the-art techniques in protecting user data by registering enhanced performance measures of Statistical Analysis, Energy Efficiency, Memory Utilization, Attack Classification, and Client Accuracy Analysis for the identification of attacks.

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Section: Resultsmentioning

confidence: 99%

“…For comparative analysis, numerous state-of-the-art studies/techniques have been used. Specifically, three challenging deep learning studies/techniques have been utilized for performance assessment including Campos et al [18] Ferrag et al [24], and Friha et al [25].…”

Section: Experimental Set-upmentioning

confidence: 99%

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

et al. 2022

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“…The applications of FL are found in various major fields such as in network optimization [40], Google advanced keyboard for prediction [41], in healthcare systems like COVID-19 detection [42] and in intrusion detection [43]. However, finding the appropriate testing model for privacy and security algorithms for IoMT is still an open research issue, and researchers are finding it difficult to either use a centralized or FL-based model to test [44]. The main difference between centralized ML and FL is that in centralized ML the learning data is uploaded to the central server, where processing is performed and information is shared among different users.…”

Section: Fl and Its Perspective In Iomtmentioning

confidence: 99%

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali¹,

Naeem²,

Tariq³

et al. 2022

Preprint

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Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to endusers as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacyrelated issues in IoMT. Afterward, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities of FL in IoMT. In the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems.

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“…Then, FL is a nascent instrument to quicken the convergence for crowdsensing patterns. For instance, in [131][132], the authors investigated the FLenhanced UE crowdsensing system, with an emphasis on confidentiality-increasing great gradient boost up with the collaboration of numerous clients like manufacturing UEs. A safe gradient aggregation procedure is constructed by incorporating homomorphic encoding with the secret distribution that inhibits the central computing server from predicting decoding outcomes before operating aggregation.…”

Section: ) Ue Crowdsensingmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Cited by 154 publications

References 157 publications

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

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