scholarly journals Using SDR Platform to Extract the RF Fingerprint of the Wireless Devices for Device Identification

2020 ◽  
Author(s):  
Ting-Yu Lin ◽  
Chia-Min Lai ◽  
Chi-Wei Chen
Keyword(s):  
Wireless Devices ◽  
Device Identification ◽  
Rf Fingerprint

scholarly journals WND-Identifier: Automated and Efficient Identification of Wireless Network Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fangzhou Zhu ◽  
Liang Liu ◽  
Simin Hu ◽  
Ting Lv ◽  
Renjun Ye
Keyword(s):  
User Interaction ◽  
Classification Model ◽  
Security And Privacy ◽  
Wireless Devices ◽  
Product Model ◽  
Widespread Application ◽  
Device Identification ◽  
Public Networks ◽  
Application Logic ◽  
Precision Device

The widespread application of wireless communication technology brings great convenience to people, but security and privacy problems also arise. To assess and guarantee the security of wireless networks and user devices, discovering and identifying wireless devices become a foremost task. Currently, effective device identification is still a challenging issue, as device fingerprinting requires huge training datasets and is difficult to expand, and rule-based identification is not accurate and reliable enough. In this paper, we propose WND-Identifier, a universal and extensible framework for the identification of wireless devices, which can generate high-precision device labels (vendor, type, and product model) efficiently without user interaction. We first introduce the concept of device-info-related network protocols. WND-Identifier makes full use of the natural language features in such protocol messages and combines with the device description in the welcome page, thereby utilizing extraction rules to generate concrete device labels. Considering that the device information in the protocol messages may be incomplete or forged, we further take advantage of the application logic independence and stability of the device-info-related protocol, so as to build a multiprotocol text classification model, which maps the device to a known label. We conduct experiments in homes and public networks and present three application scenarios to verify the effectiveness of WND-Identifier.

scholarly journals Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

2020 ◽  
Author(s):  
Zekun Chen ◽  
Linning Peng ◽  
Aiqun Hu ◽  
Hua Fu
Keyword(s):  
Prior Information ◽  
Learning Algorithm ◽  
Identification Accuracy ◽  
Generation Process ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
Device Identification ◽  
Security Issues ◽  
Adversarial Network ◽  
Rf Fingerprint

Abstract With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A USRP receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

scholarly journals Security of Wireless Devices using Biological-Inspired RF Fingerprinting Technique

2014 ◽  
pp. 311-330 ◽  
Author(s):  
Saeed ur Rehman ◽  
Shafiq Alam ◽  
Iman T. Ardekani
Keyword(s):  
Signal To Noise Ratio ◽  
Wireless Devices ◽  
Signal To Noise ◽  
Neural Network Classification ◽  
Additional Layer ◽  
Rf Fingerprinting ◽  
Wireless Device ◽  
Rf Fingerprint ◽  
Rf Fingerprints ◽  
Unknown Signal

Radio Frequency (RF) fingerprinting is a security mechanism inspired by biological fingerprint identification systems. RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. RF fingerprinting classification is performed by selecting an “unknown” signal from the pool, generating its RF fingerprint, and using a classifier to correlate the received RF fingerprint with each profile RF fingerprint stored in the database. Unlike a human biological fingerprint, RF fingerprint of a wireless device changes with the received Signal to Noise Ratio (SNR) and varies due to mobility of the transmitter/receiver and environment. The variations in the features of RF fingerprints affect the classification results of the RF fingerprinting. This chapter evaluates the performance of the KNN and neural network classification for varying SNR. Performance analysis is performed for three scenarios that correspond to the situation, when either transmitter or receiver is mobile, and SNR changes from low to high or vice versa.

scholarly journals Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

2021 ◽  
Author(s):  
Zekun Chen ◽  
Linning Peng ◽  
Aiqun Hu ◽  
Hua Fu
Keyword(s):  
Prior Information ◽  
Learning Algorithm ◽  
Software Radio ◽  
Identification Accuracy ◽  
Generation Process ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
Device Identification ◽  
Adversarial Network ◽  
Rf Fingerprint

Abstract With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A Universal Software Radio Peripheral (USRP) receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95%identification accuracy in a real environment.

scholarly journals Generative adversarial network-based rogue device identification using differential constellation trace figure

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zekun Chen ◽  
Linning Peng ◽  
Aiqun Hu ◽  
Hua Fu
Keyword(s):  
Prior Information ◽  
Learning Algorithm ◽  
Software Radio ◽  
Identification Accuracy ◽  
Generation Process ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
Device Identification ◽  
Adversarial Network ◽  
Rf Fingerprint

AbstractWith the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure generation process is initially employed to transform RF fingerprint features from time-domain waveforms to two-dimensional figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A universal software radio peripheral receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

Home Automation Using Bluetooth

2020 ◽  
pp. 675-678
Author(s):  
Mukesh Mahajan ◽  
Astha Dubey ◽  
Samruddhi Desai ◽  
Kaveri Netawate
Keyword(s):  
Temperature Sensor ◽  
Humidity Sensor ◽  
The Other ◽  
Automation System ◽  
Home Automation ◽  
Wireless Devices ◽  
Home Appliances ◽  
Home Automation System ◽  
Condition Systems

This paper reviews basically about Bluetooth based home automation system. It is controlled by PIC microcontroller. Home automation can be defined as the ability to perform tasks automatically and monitor or change status remotely. These include tasks such as turning off lights in the room, locking doors via smartphone, automate air condition systems and appliances which help in the kitchen. Now a days several wireless devices are available such as Bluetooth, Zigbee and GSM. Since Bluetooth is low in cost than the other two and hence is used more. In this paper we have described the methods of automating different home appliances using Bluetooth and pic microcontroller. Different sensors are involved in this system to advance and make it smarter. Sensors such as temperature sensor, liquid sensors, humidity sensor etc. can be used.

Blockchain based Device identification and authentication in a Smart Grid

2020 ◽  
Author(s):  
Vasudev Dehalwar ◽  
Mohan Lal Kolhe ◽  
Surendra Solanki ◽  
Mahendra Kumar Jhariya ◽  
Koki Ogura
Keyword(s):  
Smart Grid ◽  
Device Identification
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