Groundwork for Neural Network-Based Specific Emitter Identification Authentication for IoT

McGinthy, Jason M.; Wong, Lauren J.; Michaels, Alan J.

doi:10.1109/jiot.2019.2908759

Cited by 84 publications

(40 citation statements)

References 56 publications

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“…Recently, in order to further improve the authentication rate of RFFID, machine learning method is taken as a recognition algorithm [27,28,29]. However, this method consumes resources in offline samples learning due to a large number of samples training required to ensure the authentication effect.…”

Section: Background Of Rffid-mecmentioning

confidence: 99%

“…Hall et al [23] and Ureten et al [25,26] used RF fingerprinting technology to achieve wireless positioning and access control for wireless network. In order to further improve the authentication rate of RFFID, a machine learning algorithm has been introduced in extensive research as the classification algorithm of RFFID [27,28,29]. However, a machine learning algorithm needs a certain amount of computing resources to ensure a higher recognition and authentication rate.…”

Section: Introductionmentioning

confidence: 99%

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Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication

Chen

Wen

et al. 2019

Sensors

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In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the first layer, signal collection, extraction of RF fingerprint features, dynamic feature database storage, and access authentication decision are carried out by the MEC devices. In the second layer, learning features, generating decision models, and implementing machine learning algorithms for recognition are performed by the remote cloud. By this means, the authentication rate can be improved by taking advantage of the machine-learning training methods and computing resource support of the cloud. Extensive simulations are performed under the IoT application scenario. The results show that the novel method can achieve higher recognition rate than that of traditional RFFID method by using wavelet feature effectively, which demonstrates the efficiency of our proposed method.

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Section: Background Of Rffid-mecmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication

Chen

Wen

et al. 2019

Sensors

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“…The DL in IOT helps in predicting future insights, discovering new information, and taking control actions in the IOT to make it worthy for business and to improve the quality of the technology. In McGinthy et al, the difficulty in the proper trusting and the authenticating of the sensed information in the internet of things is carefully addressed using the machine learning techniques. The convolution neural network is presented in this method to secure the sensed information's with much low latency in the resource rich and the resource constrained devices.…”

Section: Related Workmentioning

confidence: 99%

Capsule network‐based data pruning in wireless sensor networks

Umamaheswari¹

2019

Int J Communication

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Summary The development of the wireless sensor networks (WSN) being deployed among numerous application for its sensing capabilities is increasing at a very fast tread. Its distributed nature and ability to extend communication even to the inaccessible areas beyond communication range that lacks human intervention has made it even more attractive in a wide space of applications. Confined with numerous sensing nodes distributed over a wide area, the WSN incurs certain limitations as it is battery powered. Many developed routing enhancements with power and energy efficiency lacked in achieving the significant improvement in the performance. So, the paper proposes a machine learning system (capsule network) and technique (data pruning) for WSN involved in the real world observations to have knowledge‐based learning from the experience for an intelligent way of handling the dynamic and real environment without the intervention of the humans. The WSN cluster‐based routing aided with capsule network and data pruning proffered in paper enables the WSN to have a prolonged network lifetime, energy efficiency, minimized delay, and enhanced throughput by reducing the energy usage and extending communication within the limited battery availability. The proposed system is validated in the network simulator and compared with the WSN without ML to check for the performance enhancements of the WSN with ML inclusions in terms of quality of service enhancements, network lifetime, packet delivery ratio, and energy to evince the efficacy of the WSN with capsule network‐based data pruning.

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“…Then, the fingerprint feature is extracted to obtain the fine features containing the individual information of the emitter; finally, compared with the database, the specific emitter of the signal is determined by the classification and recognition algorithm, and the individual emitter is identified. In recent years, the theory and practical application of emitter individual identification technology are constantly improved, and the research of fingerprint feature extraction method has made great progress [4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Fingerprint-Level Radar Simulation Modeling Method for Specific Emitter Identification

et al. 2021

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With the development of information technology for modern military confrontations, radar emitter fingerprint identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Owing to the confidentiality of military systems, most of the existing studies use simulation data for radar emitter fingerprint identification experiments and analysis. However, most of the existing modeling methods focus on the mechanism analysis of the nonlinear fingerprint characteristics of a single independent component. Its main disadvantage is that it can only represent the nonlinear fingerprint characteristics of some components in the radar emitter system but cannot fully reflect the nonlinear fingerprint characteristics of the whole radar emitter system. In this paper, a nonlinear fingerprint-level radar simulation modeling method is proposed. In contrast to the previous single component modeling method, the systematic nonlinear characteristic modeling method of this model can provide individual radar signal data under different modulation modes and working parameters, and provide experimental conditions for data support and theoretical analysis of radar emitter fingerprint identification.

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Groundwork for Neural Network-Based Specific Emitter Identification Authentication for IoT

Cited by 84 publications

References 56 publications

Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication

Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication

Capsule network‐based data pruning in wireless sensor networks

A Nonlinear Fingerprint-Level Radar Simulation Modeling Method for Specific Emitter Identification

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