PriWhisper+: An Enhanced Acoustic Short-Range Communication System for Smartphones

Xiao, Zhang; Liu, Jiqiang; Chen, Si; Kong, Yongjun; Ren, Kui

doi:10.1109/jiot.2018.2850524

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…The transmission band is recommendably allocated in the range between 8 and 20 kHz, for example, between 8 and 12 kHz. We thereby confirm the finding by Zhang et al 23 The choice of the transmission period T equal to 250 milliseconds is compliant with the speed…”

Section: Measurement Setupsupporting

confidence: 90%

Sounding and modeling of the indoor aerial acoustic transmission channel

Jung

Bruck

2021

Trans Emerging Tel Tech

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The authors verify that the indoor aerial acoustic transmission channel regarding small-scale application environments, for example, a car cabin, is a quasi-wide sense stationary uncorrelated scattering (QWSSUS) channel like radio channels. To the best knowledge of the authors, the QWSSUS feature of the indoor aerial acoustic transmission channel has never been evaluated before. Setting out from the QWSSUS concept, the channel modeling by means of correlation functions is proposed. Also, parameter sets that can be used in channel simulations are suggested. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Measurement Setupsupporting

confidence: 90%

Sounding and modeling of the indoor aerial acoustic transmission channel

Jung

Bruck

2021

Trans Emerging Tel Tech

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“…Zhang et al [13] suggested an IoT device-to-device secure acoustic short-range communication system. The authors used information theory to examine the security and suggested security enhancement strategies for acoustic communication that combine device mobility with a secret sharing system.…”

Section: Related Workmentioning

confidence: 99%

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

et al. 2022

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The authors implemented an attack scenario that involved simulating attacks to compromise node and sensor data. This research proposes a framework with algorithms that generates automated malicious commands which conform to device protocol standards and bypass compromise detection. The authors performed attack-detection testing with three different home setup simulations and referred to Accuracy of Detection, Ease of Precision, and Attack Recall, with the F1-Score as the parameter. The results obtained for anomaly detection of IoT logs and messages used K-Nearest Neighbor, Multilayer Perceptron, Logistic Regression, Random Forest, and linear Support Vector Classifier models. The attack results presented false-positive responses with and without the proposed framework and false-negative responses for different models. This research calculated Precision, Accuracy, F1-Score, and Recall as attack-detection performance models. Finally, the authors evaluated the performance of the proposed IoT communication protocol attack framework by evaluating a range of anomalies and compared them with the maliciously generated log messages. IoT Home #1 results in which the model involving an IP Camera and NAS device traffic displayed 97.7% Accuracy, 96.54% Precision, 97.29% Recall, and 96.88% F1-Score. This demonstrated that the model classified the Home #1 dataset consistently.

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“…Also, [42] proposes a system for secure mobile devices pairing based on audio fingerprints extracted from the recorded audio data. An acoustic communication mechanism for smartphones based on jamming signals is proposed in [43]. In [44] a two factor authentication system is proposed which works at high frequencies, i.e., between 18kHz and 20kHz.…”

Section: Papermentioning

confidence: 99%

Fingerprinting Smartphones Based on Microphone Characteristics From Environment Affected Recordings

et al. 2022

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Fingerprinting devices based on unique characteristics of their sensors is an important research direction nowadays due to its immediate impact on non-interactive authentications and no less due to privacy implications. In this work, we investigate smartphone fingerprints obtained from microphone data based on recordings containing human speech, environmental sounds and several live recordings performed outdoors. To comply with real-world circumstances, we also consider the presence of several types of noise that is specific to the scenarios which we address, e.g., traffic and market noise at distinct volumes, which may reduce the reliability of the data. We analyze several classification techniques based on traditional machine learning algorithms and more advanced deep learning architectures that are put to test in recognizing devices from the recordings they made. The results indicate that the classical Linear Discriminant classifier and a deep-learning Convolutional Neural Network have comparable success rates while outperforming all the rest of the algorithms.

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PriWhisper+: An Enhanced Acoustic Short-Range Communication System for Smartphones

Cited by 9 publications

References 21 publications

Sounding and modeling of the indoor aerial acoustic transmission channel

Sounding and modeling of the indoor aerial acoustic transmission channel

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

Fingerprinting Smartphones Based on Microphone Characteristics From Environment Affected Recordings

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