A Hierarchical Federated Learning-Based Intrusion Detection System for 5G Smart Grids

Sun, Xiaoying; Tang, Zhijun; Du, Mengxuan; Deng, Chaoping; Wang, Lin; Chen, Jinshan; Qi, Qi; Zheng, Haifeng

doi:10.3390/electronics11162627

Cited by 20 publications

(6 citation statements)

References 33 publications

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“…Among the machine learning methods (in terms of detecting network attacks), the support vector machine method has the worst performance in terms of accuracy index. The proposed method is compared with machine learning and deep learning findings of previous research [49]. In [49], federated hierarchical learning is used to detect attacks on smart grids.…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…The proposed method is compared with machine learning and deep learning findings of previous research [49]. In [49], federated hierarchical learning is used to detect attacks on smart grids. Table 4 compares the proposed method with SVM, LR, KNN, MultinomialNB, and deep learning methods, such as GRU+MLP, DNN-3, Transformer-IDM, and DNN-16.…”

Section: Evaluation Resultsmentioning

confidence: 99%

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Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Mhmood,

Ergül,

Rahebi

2023

SIViP

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Cyber attacks against Smart Grids (SG) have harmful effects. The first function of a defensive system is to provide an intelligent system to detect intrusions. The nature of attacks against smart grids is very complex, so the intrusion detection system must be able to detect complex attacks. Lack of balancing and optimization of deep learning methods are the main challenges for many intrusion detection systems. This research presents an intelligent intrusion detection system for a smart grid based on Game Theory, Swarm Intelligence, and Deep Learning (DL). First, the proposed method balances the training samples with a conditional DL technique based on Game Theory and CGAN. Secondly, the Aquila Optimizer (AO) algorithm selects features. The third step involves mapping the selected features on the dataset and coding reduced-dimension samples into RGB color images, which are used to train the VGG19 neural network. In the fourth step, the AO algorithm optimally adjusts meta-parameters to reduce the error of the VGG19 neural network. Tests performed on the NSL-KDD dataset show that the proposed method's accuracy, sensitivity, and precision in detecting attacks are 99.82%, 99.69%, and 99.76%, respectively. The CGAN method balances the dataset and increases the accuracy, sensitivity, and precision of the proposed method compared to the GAN method in detecting attacks on the smart grid. Experiments show that the proposed method more accurately detects attacks than deep learning methods such as VGG19, CNN-GRU, CNN-GRU-FL, LSTM, and CNN.

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Section: Evaluation Resultsmentioning

confidence: 99%

Section: Evaluation Resultsmentioning

confidence: 99%

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Mhmood,

Ergül,

Rahebi

2023

SIViP

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“…Te accuracy of the proposed model was 93.6%. For intrusion detection in 5G smart grids, Sun et al [40] designed a neural network that utilized a transformer and hierarchical federated learning. Te authors reported an accuracy of 99.48%.…”

Section: Federated Learning For Security Monitoring In 5gmentioning

confidence: 99%

Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model

Maiga,

Ataro,

Githinji

2024

Journal of Electrical and Computer Engineering

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The cloudification of telecommunication network functions with 5G is a novelty that offers higher performance than that of previous generations. However, these virtual network functions (VNFs) are exposed to internet threats when hosted in the cloud, resulting in new security challenges. Another fact is that many VNFs vendors with different security policies will be implied in 5G deployment, creating a heterogeneous 5G network. The authorities also require data privacy enhancement in 5G deployment and there is the fact that mobile operators need to inspect data for malicious traffic detection. In this situation, how can network traffic inspections be conducted effectively without infringing on data privacy? This study addresses this gap by proposing a novel state-of-the-art hybrid deep neural network that combines a convolutional neural network (CNN) stacked to bidirectional long short-term memory (BiLSTM) and unidirectional long short-term memory (LSTM) for the deep inspection of network flow for malicious traffic detection. The approach utilizes federated learning (FL) to facilitate multiple VNFs vendors to collaboratively train the proposed model without sharing VNFs’ raw data, which can mitigate the risk of data privacy violation. The proposed framework incorporates transport layer security (TLS) encryption to prevent data tempering or man-in-the-middle attacks between VNFs. The framework was validated through simulation using open-access benchmark datasets (InSDN and CICIDS2017). They achieved 99.99% and 99.58% accuracy and 0.048% and 0.617% false-positive rates for the InSDN and CICIDS2017 datasets, respectively, for FL. This study demonstrates the potential of hybrid deep learning-based FL for heterogeneous 5G network VNFs security monitoring.

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“…The authors in [35] proposed a transformer-based intrusion detection model (Transformer-IDM) in which the transformer and feature exaction layers are leveraged to process categorical and numerical features in order to improve the detection performance. They also introduced a hierarchical federated learning intrusion detection system to collaboratively train Transformer-IDM to protect user privacy in the core networks.…”

Section: Related Workmentioning

confidence: 99%

A two-stage cyber attack detection and classification system for smart grids

Alani,

Mauri,

Damiani

2023

Internet of Things

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A Hierarchical Federated Learning-Based Intrusion Detection System for 5G Smart Grids

Cited by 20 publications

References 33 publications

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model

A two-stage cyber attack detection and classification system for smart grids

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