Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

Wu, Yulei; Ma, Yanfei; Dai, Hong-Ning; Wang, Hao

doi:10.1016/j.comnet.2020.107743

Cited by 37 publications

(15 citation statements)

References 154 publications

(164 reference statements)

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“…In particular, this study collects the Energy Information Administration (EIA) energy disturbance events (containing outages) occurred from January 2011 to December 2013 among 26,304 data samples 14 as well as weather measurement data according to historical observations 15 at the same period and weather stations at the same region. Moreover, the work [113] presents a labeled energy-consumption dataset from 500 energy customers in Ireland 16 though the labelled dataset is not officially released (available upon request to the authors).…”

Section: B Contextualmentioning

confidence: 99%

“…Anomaly detection is of paramount importance in IIoT [10]- [15]. On the one hand, traditional isolated industry systems are now exposed to the public access due to the introduction of IIoT [16]- [19]. The sensors, instruments, and other IoT devices that were originally designed with little security mechanisms can be easily compromised by malicious users like attackers [4], [16], [20].…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, traditional isolated industry systems are now exposed to the public access due to the introduction of IIoT [16]- [19]. The sensors, instruments, and other IoT devices that were originally designed with little security mechanisms can be easily compromised by malicious users like attackers [4], [16], [20]. Efficient detection of such anomalies is crucial to the security of IIoT and the success of related businesses.…”

Section: Introductionmentioning

confidence: 99%

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Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

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The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

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Section: B Contextualmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

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“…There have been many surveys into privacy concerns and preservation technologies in different technologies: 6G IoT networks [240], 6G AI-enabled applications [6], autonomous driving [241], big data and cloud [242]. The techniques all meet common goals: (1) reduce identity linkage in data collection; (2) increase secure data storage; and (3) control over data sharing and use.…”

Section: B State-of-the-art Privacy Preservation Techniques and Visio...mentioning

confidence: 99%

Security and privacy for 6G: A survey on prospective technologies and challenges

Nguyen,

Lin,

Cheng

et al. 2021

Preprint

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Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses.Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

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“…W ITH the rapid development of Industrial 4.0, a distributed Internet of Things (IoT) system is becoming a dominant architecture in industrial IoT, which enables elastic interconnection of automation and data analytics across IoT networks [1], [2]. However, security in distributed IoT devices is highly threatened by malicious intruders.…”

Section: Introductionmentioning

confidence: 99%

Variational Few-Shot Learning for Microservice-Oriented Intrusion Detection in Distributed Industrial IoT

Liang

Zhou

et al. 2022

IEEE Trans. Ind. Inf.

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Along with the popularity of the Internet of Things (IoT) techniques with several computational paradigms, such as cloud and edge computing, microservice has been viewed as a promising architecture in largescale application design and deployment. Due to the limited computing ability of edge devices in distributed IoT, only a small scale of data can be used for model training. In addition, most of the machine-learning-based intrusion detection methods are insufficient when dealing with imbalanced dataset under limited computing resources. In this article, we propose an optimized intra/inter-class-structure-based variational few-shot learning (OICS-VFSL) model to overcome a specific out-of-distribution problem in imbalanced learning, and to improve the microservice-oriented intrusion detection in distributed IoT systems. Following a newly designed VFSL framework, an intra/inter-class optimization scheme is developed using reconstructed feature embeddings, in which the intra-class distance is optimized based on the approximation during a variation Bayesian process, while the inter-class distance is optimized based on the maximization of similarities during a feature concatenation process. An intelligent intrusion detection algorithm is, then, introduced to improve the multiclass classification via a nonlinear neural network. Evaluation experiments are conducted using two public datasets to demonstrate the effectiveness of our proposed model, especially in detecting novel attacks with extremely imbalanced data, compared with four baseline methods.

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Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks

Cited by 37 publications

References 154 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Security and privacy for 6G: A survey on prospective technologies and challenges

Variational Few-Shot Learning for Microservice-Oriented Intrusion Detection in Distributed Industrial IoT

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