Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0

Khoa, Tran Viet; Saputra, Yuris Mulya; Hoang, Dinh Thai; Trung, Nguyen Linh; Nguyen, Diep N.; Hà, Nguyễn Việt; Dutkiewicz, Eryk

doi:10.1109/wcnc45663.2020.9120761

Cited by 53 publications

(32 citation statements)

References 32 publications

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“…Experiments with Mirai malware on a testbed confirm a high detection rate with 95.6% with low learning time, compared to the centralized learning approach at an IoT server. Another FLbased attack detection scheme is also considered in [83] where smart filters are built at IoT gateways to identity and prevent cyberattacks in industry 4.0. More specifically, each filter is built based on a DNN that trains local datasets collected from its subnetwork such as a smart farming or an energy plant.…”

Section: Fl For Iot Attack Detectionmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

578

251

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Section: Fl For Iot Attack Detectionmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

578

251

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“…Network Intrusion Detection System (NIDS) is often designed for specific use cases. In the literature, Federated Learning (FL) method has been proposed for intrusion detection in Wireless Edge Network (WEN) [32], [33], IoT [21]- [23], [34]- [39], Industrial IoT (IIoT) [24], [40]- [42], industrial Cyber-Physical System (CPS) [43], Medical CPS [44], Wireless Fidelity (Wi-Fi) network [45], large-scale distributed Local Area Network (LAN) [46], [47], satellite-terrestrial integrated networks [48], Cloud [49], edge computing [50], vehicular network [26], [51], [52]. We acknowledge that FL methods have been proposed for intrusion detection in IoT networks [21]- [23], [34]- [39].…”

Section: Review Of Related Workmentioning

confidence: 99%

Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT-Edge Devices

Popoola

Ande²,

Adebisi

et al. 2022

IEEE Internet Things J.

167

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Deep Learning (DL) has been widely proposed for botnet attack detection in Internet of Things (IoT) networks. However, the traditional Centralized DL (CDL) method cannot be used to detect previously unknown (zero-day) botnet attack without breaching the data privacy rights of the users. In this paper, we propose Federated Deep Learning (FDL) method for zero-day botnet attack detection to avoid data privacy leakage in IoT edge devices. In this method, an optimal Deep Neural Network (DNN) architecture is employed for network traffic classification. A model parameter server remotely coordinates the independent training of the DNN models in multiple IoT edge devices, while Federated Averaging (FedAvg) algorithm is used to aggregate local model updates. A global DNN model is produced after a number of communication rounds between the model parameter server and the IoT edge devices. Zero-day botnet attack scenarios in IoT edge devices is simulated with the Bot-IoT and N-BaIoT data sets. Experiment results show that FDL model: (a) detects zero-day botnet attacks with high classification performance; (b) guarantees data privacy and security; (c) has low communication overhead (d) requires low memory space for the storage of training data; and (e) has low network latency. Therefore, FDL method outperformed CDL, Localized DL, and Distributed DL methods in this application scenario. Index Terms-Cybersecurity, botnet detection, federated learning, deep learning, deep neural network, Internet of Things. I. INTRODUCTION B OTNET attack is a serious cyber security challenge facing the Internet of Things (IoT) [1]-[3]. In our context, a botnet is a network of compromised devices that is used to launch cyber attack against critical infrastructures [4]. This cyber attack may be in form of Denial of Service (DoS), Distributed DoS (DDoS), reconnaissance, or data theft [5].

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“…The FTM framework is claimed to achieve high accuracy due to the homomorphic attribution. Khoa et al [64] presented an IDS based on collaborative learning which can be applied effectively in the Industrial IoT and Industry 4.0. The proposed system builds intelligent "filters" for deployment at IoT gateways to quickly identify and prevent cyberattacks.…”

Section: B Secure Industrial Internet Of Thingsmentioning

confidence: 99%

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

Ferrag¹,

et al. 2021

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In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

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Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0

Cited by 53 publications

References 32 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT-Edge Devices

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

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