Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT

Shi, Cong; Liu, Jian; Liu, Hongbo; Chen, Yingying

doi:10.1145/3084041.3084061

Cited by 235 publications

(113 citation statements)

References 23 publications

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“…Using these features, the authors developed a DL model (i.e. Deep Neural Network (DNN)) to identify the daily human activity distinctiveness of each individual and subsequently generate a fingerprint for each user, called Wi-Fi fingerprint, to capture the distinct characteristics of different users; the proposed DLbased authentication method exhibited high accuracy [217]. This study validates the potential application of DL algorithms in constructing authentication systems.…”

Section: Edlnsmentioning

confidence: 59%

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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

835

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: Edlnsmentioning

confidence: 59%

“…Shi, Liu, Liu and Chen [217] proved that the present Wi-Fi signals generated by IoT objects can be adopted to detect distinctive human behavioural and physiological features and can be utilised to authenticate individuals on the basis of an…”

Section: A Perception Layermentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

835

333

View full text Add to dashboard Cite

show abstract

“…Real-time processing: Most existing deep learning based security schemes such as the DNN-based authentication in [4] and the RNN-based malware detection in [11] require long training time and are too complicated to be implemented in the practical MCS systems for real-time processing. The widely used hardware for deep learning computation such as graphics processing units (GPUs), is not applicable for most mobile devices such as smartphones in MCS systems.…”

Section: Discussionmentioning

confidence: 99%

Secure mobile crowdsensing based on deep learning

Xiao

Jiang

et al. 2018

China Commun.

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In order to stimulate secure sensing for Internet of Things (IoT) applications such as healthcare and traffic monitoring, mobile crowdsensing (MCS) systems have to address security threats, such as jamming, spoofing and faked sensing attacks, during both the sensing and the information exchange processes in large-scale dynamic and heterogenous networks. In this article, we investigate secure mobile crowdsensing and present how to use deep learning (DL) methods such as stacked autoencoder (SAE), deep neural network (DNN), and convolutional neural network (CNN) to improve the MCS security approaches including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DL-based approaches compared with traditional security schemes and identify the challenges that need to be addressed to implement them in practical MCS systems.

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“…2) DL-based authentication and access control in IoT: Shi et al [87] proposed a user authentication technique for IoT based on human physiological activities through WiFi signals. The proposed authentication scheme is based on both activity recognition and human identification.…”

Section: A Authentication and Access Control In Iotmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

593

270

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The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.

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Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT

Cited by 235 publications

References 23 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Secure mobile crowdsensing based on deep learning

Machine Learning in IoT Security: Current Solutions and Future Challenges

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