Federated deep learning for anomaly detection in the internet of things

Wang, Yonghong; Javaheri, Zahra; Almutairi, Laila; Moghadamnejad, Navid; Younes, Osama

doi:10.1016/j.compeleceng.2023.108651

Cited by 40 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te authors discussed its applicability to edge computing and caching, spectrum management, and 5G core networks. Te authors of [19][20][21] used federated learning to detect malware and anomalies in Internet of Tings (IoT) networks and devices. As is evident, federated learning has been utilized in numerous areas; however, its potential for future telecommunication network security, such as 5G, is yet to be explored.…”

Section: Federatedmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Federatedmentioning

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

View full text Add to dashboard Cite

show abstract

“…We have only included studies (including technical articles and excluding review-type articles) focused on both areas. Table 7 summarizes the methodologies and results of the applications [115][116][117][118][119][120][121][122][123][124][125][126][127].…”

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

“…To ensure an up-to-date discussion, only works published in 2023 were included in Table 7. In general, existing works have tackled CPS and IoT applications using classification [115,[118][119][120][121][122][123][124]127], regression [125], and deep learning [118][119][120][121][122]126] approaches. More investigations can be conducted using clustering, transfer learning, and data generation algorithms.…”

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

“…Bidirectional long short-term memory network with an attention mechanism Root-mean-square error of 0.011 and a mean average error of 0.002 [120] Anomaly detection for network incursions Federated deep neural network True negative rate of 97.9%, true positive rate of 99.7%, and accuracy of 99.7% Many studies have considered applications to detect security threats such as network attacks [116], network intrusion [118], anomaly [120,127], network intrusion [121,124], malware [122], and attack paths [123]. More discussion is presented on security threats and common tools in Section 5.…”

Section: Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Chui¹,

Gupta

Liu³

et al. 2023

Information

View full text Add to dashboard Cite

The smart city vision has driven the rapid development and advancement of interconnected technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this paper, various aspects of IoT and CPS in recent years (from 2013 to May 2023) are surveyed. It first begins with industry standards which ensure cost-effective solutions and interoperability. With ever-growing big data, tremendous undiscovered knowledge can be mined to be transformed into useful applications. Machine learning algorithms are taking the lead to achieve various target applications with formulations such as classification, clustering, regression, prediction, and anomaly detection. Notably, attention has shifted from traditional machine learning algorithms to advanced algorithms, including deep learning, transfer learning, and data generation algorithms, to provide more accurate models. In recent years, there has been an increasing need for advanced security techniques and defense strategies to detect and prevent the IoT and CPS from being attacked. Research challenges and future directions are summarized. We hope that more researchers can conduct more studies on the IoT and on CPS.

show abstract

“…This reduces the amount of work that people have to do and makes the digital world easier to access, more productive, and easier to control. Because the IoT ecosystem is made up of many different types of devices, it is very vulnerable to cyberattacks that take advantage of the system’s weaknesses to stop it from working [ 2 , 3 ]. Cyber threats are getting more complex and dangerous, and the IoT infrastructure is proliferating [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection

Zegarra Rodríguez,

Daniel Okey,

Maidin

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks resulting from malicious network flows during data exchange between the connected devices. Various security threats have shown high adverse effects on the availability, functionality, and usability of the devices among which denial of service (DoS) and distributed denial of service (DDoS), which attempt to exhaust the capacity of the IoT network (gateway), thereby causing failure in the functionality of the system have been more pronounced. Various machine learning and deep learning algorithms have been used to propose intelligent intrusion detection systems (IDS) to mitigate the challenging effects of these network threats. One concern is that although deep learning algorithms have shown good accuracy results on tabular data, not all deep learning algorithms can perform well on tabular datasets, which happen to be the most commonly available format of datasets for machine learning tasks. Again, there is also the challenge of model explainability and feature selection, which affect model performance. In this regard, we propose a model for IDS that uses attentive mechanisms to automatically select salient features from a dataset to train the IDS model and provide explainable results, the TabNet-IDS. We implement the proposed model using the TabNet algorithm based on PyTorch which is a deep-learning framework. The results obtained show that the TabNet architecture can be used on tabular datasets for IoT security to achieve good results comparable to those of neural networks, reaching an accuracy of 97% on CIC-IDS2017, 95% on CSE-CICIDS2018 and 98% on CIC-DDoS2019 datasets.

show abstract

Federated deep learning for anomaly detection in the internet of things

Cited by 40 publications

References 23 publications

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

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

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection

Contact Info

Product

Resources

About