Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Li, Kun; Zhou, Huachun; Tu, Zhe; Wang, Weilin; Zhang, Hongke

doi:10.1109/access.2020.3041641

Cited by 56 publications

(29 citation statements)

References 25 publications

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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%

“…Cetin et al [45] employed Stacked Autoencoder (SAE). Sun et al [46], [47] and Li et al [48] adopted CNN. Qin et al [33] proposed Binarised Neural Network (BNN).…”

Section: Review Of Related Workmentioning

confidence: 99%

“…FL methods are simulated with relevant network intrusion dataset(s) to evaluate their performance. So far, FL methods have been simulated with WSN-DS [32], KDDCup99 [32], [49], [54], CICIDS2017 [32], [33], NSL-KDD [21], [23], [36], GPWST [43], AWID [45], ISCX2014 [33], UNSW-NB15 [29], [50], and private datasets [22], [46]- [48]. However, these network intrusion datasets did not contain samples of IoT network traffic.…”

Section: Review Of Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Popoola

Ande²,

Adebisi

et al. 2022

IEEE Internet Things J.

191

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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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Section: Review Of Related Workmentioning

confidence: 99%

“…Cetin et al [45] employed Stacked Autoencoder (SAE). Sun et al [46], [47] and Li et al [48] adopted CNN. Qin et al [33] proposed Binarised Neural Network (BNN).…”

Section: Review Of Related Workmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Popoola

Ande²,

Adebisi

et al. 2022

IEEE Internet Things J.

191

View full text Add to dashboard Cite

show abstract

“…Specifically, the proposed scheme exploits a block-design-based key agreement to achieve efficient communication among the satellites. Besides, Li et al in [25] propose a network detection system in STIN, which analyzes and resists malicious traffic, especially the distributed denial-of-service (DDoS) attacks. By exploiting an ID-based framework, Gowri et al in [26] propose an efficient identitybased authentication scheme for the Automatic dependent surveillance-broadcast (ADS-B) system, which is pairing-free and supports batch verification.…”

Section: B Communication Overheadsmentioning

confidence: 99%

Achieving Efficient and Secure Handover in LEO Constellation-Assisted Beyond 5G Networks

Kong

Yin

2022

IEEE Open J. Commun. Soc.

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Ubiquitous connection to modern vehicles is mandatory to support diverse intelligent functions, including autonomous driving, telediagnosis, infotainment services, and others. Since the deployment of the cellular access points is still scarce in rural areas, the low earth orbit (LEO) constellationbased communication is believed to provide a realistic alternative. However, due to the long propagation delay and limited satellite on-board processing ability, the design of security protocols in LEO constellations remain challenging. Aiming at these challenges, we propose a secure user access and inter-satellite handover mechanism, which achieves the control-and userplane key separation. Specifically, our proposed scheme exploits an identity-based encryption scheme with proxy re-encryption to achieve the key establishments with high efficiency, and it also achieves the highly efficient secure batch handover with the assistance of a stack. Detailed analysis is performed to demonstrate the security properties of our proposed scheme, in terms of confidentiality, authentication, and forward/backward key separation. Furthermore, simulation results illustrate that our proposed scheme achieves high computational complexity and communication overheads in comparison with a traditional scheme.

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“…The proposed system is implemented and evaluated using Python on Google Colab with the real-world dataset (NSL-KDD), which the results show 98.11% detection accuracy with federated mimic learning compared to centralized machine learning-based IDSs. To address the need for securing traffic and maintaining privacy in heterogeneous networks, Li et al [88] designed a distributed an IDS based on federated learning for satellite-terrestrial integrated networks for analyzing and blocking harmful traffic, especially distributed denial of service (DDoS) attacks. The proposed IDS uses two technologies, namely, 1) homomorphic encryption to provide secure multi-party computing in federated learning and 2) convolutional neural network for achieving higher recognition accuracy.…”

Section: A Federated Learning-based Anomaly Detectionmentioning

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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Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Cited by 56 publications

References 25 publications

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

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

Achieving Efficient and Secure Handover in LEO Constellation-Assisted Beyond 5G Networks

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

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