Analyzing using Software Defined Radios as Wireless Sensor Network Inspection and Testing Devices: An Internet of Things Penetration Testing Perspective

O'Mahony, George D.; Harris, Philip J.; Murphy, Colin C.

doi:10.1109/giots49054.2020.9119606

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…The commercial XBee nodes are controlled using the Python3 "digi-xbee" library, powered using a Raspberry Pi and transmit real data by utilizing the Raspberry Pi SenseHat sensor. This operation was validated in an IoT ZigBee testbed in [45]. Hence, the Raspberry Pi embedded device operates both the SDRs and XBee nodes and enables environmental data acquisition using the SenseHat sensor.…”

Section: Experimental Data Collectionmentioning

confidence: 99%

“…The strategy, as shown in Figure 3, was built around an Analog Devices Pluto SDR and Raspberry Pi design to maximize the data collection process using low-cost hardware. The Pluto SDR was selected as it is relatively low cost, has Python and Matlab libraries, previous experimental success [45] and encompassed suitable parameters for operating as a data receiver and controlled data transmitter. These parameters include a 12-bit analog to digital converter (ADC) resolution, a 20 MHz maximum attainable bandwidth and frequency and sample rate ranges of 325 MHz-3.8 GHz and 65.2 ksps-61.44 Msps, respectively.…”

Section: Experimental Data Collectionmentioning

confidence: 99%

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Developing a Low-Order Statistical Feature Set Based on Received Samples for Signal Classification in Wireless Sensor Networks and Edge Devices

O'Mahony

McCarthy

Harris³

et al. 2021

IoT

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Classifying fluctuating operating wireless environments can be crucial for successfully delivering authentic and confidential packets and for identifying legitimate signals. This study utilizes raw in-phase (I) and quadrature-phase (Q) samples, exclusively, to develop a low-order statistical feature set for wireless signal classification. Edge devices making decentralized decisions from I/Q sample analysis is beneficial. Implementing appropriate security and transmitting mechanisms, reducing retransmissions and increasing energy efficiency are examples. Wireless sensor networks (WSNs) and their Internet of Things (IoT) utilization emphasize the significance of this time series classification problem. Here, I/Q samples of typical WSN and industrial, scientific and medical band transmissions are collected in a live operating environment. Analog Pluto software-defined radios and Raspberry Pi devices are utilized to achieve a low-cost yet high-performance testbed. Features are extracted from Matlab-based statistical analysis of the I/Q samples across time, frequency (fast Fourier transform) and space (probability density function). Noise, ZigBee, continuous wave jamming, WiFi and Bluetooth signal data are examined. Supervised machine learning approaches, including support vector machines, Random Forest, XGBoost, k nearest neighbors and a deep neural network (DNN), evaluate the developed feature set. The optimal approach is determined as an XGBoost/SVM classifier. This classifier achieves similar accuracy and generalization results, on unseen data, to the DNN, but for a fraction of time and computation requirements. Compared to existing approaches, this study’s principal contribution is the developed low-order feature set that achieves signal classification without prior network knowledge or channel assumptions and is validated in a real-world wireless operating environment. The feature set can extend the development of resource-constrained edge devices as it is widely deployable due to only requiring received I/Q samples and these features are warranted as IoT devices become widely used in various modern applications.

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Section: Experimental Data Collectionmentioning

confidence: 99%

Section: Experimental Data Collectionmentioning

confidence: 99%

Developing a Low-Order Statistical Feature Set Based on Received Samples for Signal Classification in Wireless Sensor Networks and Edge Devices

O'Mahony

McCarthy

Harris³

et al. 2021

IoT

View full text Add to dashboard Cite

show abstract

“…Thus the PT is processed in the manner of both wired and wireless model. George D. O'Mahony et al[65] developed radios framework based on software defined model, this model has provided whole security for entire system to improve the system performance. It evaluation is shown infig.4.…”

mentioning

confidence: 99%

A Systematic Broad Review on Software Security Testing in Different Ways

Suman¹

2020

IJCSMC

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Research on the Application of Penetration Testing Frameworks in Blockchain Security

Chen,

Zhang,

Liang

et al. 2024

Computational and Experimental Simulations in Engineering

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Analyzing using Software Defined Radios as Wireless Sensor Network Inspection and Testing Devices: An Internet of Things Penetration Testing Perspective

Cited by 7 publications

References 13 publications

Developing a Low-Order Statistical Feature Set Based on Received Samples for Signal Classification in Wireless Sensor Networks and Edge Devices

Developing a Low-Order Statistical Feature Set Based on Received Samples for Signal Classification in Wireless Sensor Networks and Edge Devices

A Systematic Broad Review on Software Security Testing in Different Ways

Research on the Application of Penetration Testing Frameworks in Blockchain Security

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