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Domain-Adaptive Device Fingerprints for Network Access Authentication Through Multifractal Dimension Representation
[article]
2023
arXiv
pre-print
Preserved Fulltext
RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a potential solution for automated network access authentication. ...
Some examples of a domain change include varying the device location or environment and varying the time or day of data collection. ...
HARDWARE IMPAIRMENTS AND THEIR IMPACT ON THE VFDT OF THE RECEIVED IQ SIGNALS A. ...
arXiv:2308.07925v1
fatcat:xq65c5emzrflbp3ea7o2kmahhq
On the Domain Generalizability of RF Fingerprints Through Multifractal Dimension Representation
[article]
2024
arXiv
pre-print
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2023
2023 IEEE Conference on Communications and Network Security (CNS)
unpublished
doi:10.1109/cns59707.2023.10289056 fatcat:ei4jekift5dl5gezk5yvgj6q4i
doi:10.1109/cns59707.2023.10289056 fatcat:ei4jekift5dl5gezk5yvgj6q4i
RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a possible method for enabling secure device identification and authentication. ...
Some examples of a domain change include varying the location or environment of the device and varying the time or day of the data collection. ...
Each device is powered on given an initial 20 minute warm up period before data collection begins to ensure hardware stabilization [22] . ...
arXiv:2402.10044v1
fatcat:kyxxqli5sbg4daqs3wkigdl2cu
Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security
[article]
2024
arXiv
pre-print
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Our findings demonstrate the superiority of the proposed framework in enhancing the accuracy, robustness, and adaptability of deep learning-based methods, thus offering a pioneering solution for enabling ...
This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework designed to serve as the device authentication layer within the ZT architecture, with a focus on resource-constrained ...
We want to emphasize here the importance of waiting until the end of the warm-up period of the devices' hardware before starting the data collection process to ensure robust and consistent measurements ...
arXiv:2402.05332v1
fatcat:djywy7dgvvdvziaxd43w4c4syu
ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation
[article]
2023
arXiv
pre-print
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Deep learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a potential solution for enabling key wireless network applications and services, such as spectrum policy enforcement and ...
It also improves the classification accuracy by up to 9% on long-term temporal adaptation. ...
The lack of behavioral comprehension of RFFs and deep learning networks opens up the floor to several hypotheses to explain the exposed sensitivity of deep learning models when the domain changes. ...
arXiv:2301.12360v1
fatcat:x7s6fj4ysjf6xfhc3aisjjbboi
Clustering Wi-Fi fingerprints for indoor–outdoor detection
2018
Wireless networks
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2019 pre-print arXiv:1908.00758v1 fatcat:nkdu2lg3ufbcrletafranycmfy
Moreover, future indoor positioning systems are likely to use Wi-Fi fingerprints, and therefore Wi-Fi receivers will be on most of the time. ...
Using various machine learning algorithms, we train a supervised classifier based on features extracted from the raw fingerprints, clusters, and cluster transition graph. ...
a warm-up time of a minute in most cases. ...
doi:10.1007/s11276-018-1753-9
fatcat:pd7r7zs4mjhkrmmvbfe3aplmbe
Considerations, Advances, and Challenges Associated with the Use of Specific Emitter Identification in the Security of Internet of Things Deployments: A Survey
2023
Information
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This attention is largely due to SEI's demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural ...
(RF) front end. ...
Acknowledgments: The views, analysis, and conclusions presented in this article are those of the authors and should not be interpreted or construed as representing the official policies-expressed or implied-of ...
doi:10.3390/info14090479
fatcat:qgyn7zlby5b4fhuda25bgwi6mu
Electromagnetic radiation-based IC device identification and verification using deep learning
2020
EURASIP Journal on Wireless Communications and Networking
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Device identification based on deep residual networks and radio frequency is a technology applicable to the physical layer, which can improve the security of integrated circuit (IC)-based multifactor authentication ...
Therefore, the identification of radio frequency equipment based on deep residual network is very suitable as a countermeasure for implementing the device cloning technology and is expected to be related ...
Authors' contributions ZH, XJ, and ZF conceived of the study and participated in its design. CX participated in the data collection work. SS participated in the design of the study. ...
doi:10.1186/s13638-020-01808-z
fatcat:rzlkyj5yqzdofcrbeojeods3he
Some aspects of physical prototyping in Pervasive Computing
[article]
2018
arXiv
pre-print
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The main connecting theme is the physical layer of widely deployed sensors in Pervasive Computing domains. In particular, we have focused on the RF-channel or on ambient audio. ...
One thing that we have learned from the work on these physical layer algorithms was that the signals we work on are fragile and perceptive to physical environmental changes. ...
In particular, the hardware-based audio-pre-processing on one of the phones and the nonreal-time capability of the operating system posed greatest difficulties. ...
arXiv:1801.06326v1
fatcat:yhieujb32vc4jdcasbotbgkod4
Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research
2021
Sustainability
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This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing. ...
In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly ...
Acknowledgments: The authors are thankful to the anonymous reviewers for their careful read.
Conflicts of Interest: The authors declare that no conflict of interest exist. ...
doi:10.3390/su13158600
fatcat:kouqjwoo4nchhhuwnhvz2ttdvm
Deep Learning in Mobile and Wireless Networking: A Survey
[article]
2019
arXiv
pre-print
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Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. ...
The recent success of deep learning underpins new and powerful tools that tackle problems in this space. ...
[311]
Indoor fingerprinting
CSI
RBM
First deep learning driven indoor
localization based on CSI
Wang et al. ...
arXiv:1803.04311v3
fatcat:awuvyviarvbr5kd5ilqndpfsde
Deep Learning in Mobile and Wireless Networking: A Survey
2019
IEEE Communications Surveys and Tutorials
Preserved Fulltext
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The file type is application/pdf.
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. ...
The recent success of deep learning underpins new and powerful tools that tackle problems in this space. ...
[314]
Indoor fingerprinting
CSI
Device-based
RBM
First deep learning driven indoor
localization based on CSI
Wang et al. ...
doi:10.1109/comst.2019.2904897
fatcat:xmmrndjbsfdetpa5ef5e3v4xda
Second white paper on community guidelines on the use, value and applicability of emerging technologies in climate and weather applications - Deliverable D2.7
2022
Zenodo
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of hardware accelerators and machine learning for weather and climate modelling. ...
In order to track some of the earliest work in the field, a second workshop on Emerging Technologies for Weather and Climate Modelling was held on the 7th of October as a virtual event. ...
A deep neural network based on a CNN and a LSTM recurrent module was proposed in (Miao et al., 2019) to estimate precipitation based on well-resolved atmospheric dynamical fields. ...
doi:10.5281/zenodo.7353443
fatcat:lgk33pffabh6bnlhxrxemzdjsm
Security Threats in ZigBee-Enabled Systems: Vulnerability Evaluation, Practical Experiments, Countermeasures, and Lessons Learned
2013
2013 46th Hawaii International Conference on System Sciences
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The attack scenarios are based on utilizing several vulnerabilities found from the main security components of ZigBee technology. ...
The first attack is based on sabotaging the ZigBee End-Device by sending a special signal that makes it wake-up constantly until the battery runs out. ...
Acknowledgements This research work was funded by the European Union Artemis project Design, Monitoring, and Operation of Adaptive Networked Embedded Systems (DEMANES). ...
doi:10.1109/hicss.2013.475
dblp:conf/hicss/VidgrenHPRT13
fatcat:umo7knm6irhh7kpyxaa3eha4na
Large-Scale Chemical Language Representations Capture Molecular Structure and Properties
[article]
2022
arXiv
pre-print
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Models based on machine learning can enable accurate and fast molecular property predictions, which is of interest in drug discovery and material design. ...
Recently, unsupervised transformer-based language models pretrained on a large unlabelled corpus have produced state-of-the-art results in many downstream natural language processing tasks. ...
The supervised baselines consist of shallow machine learning models trained on molecular fingerprints (RF and SVM in Table 1 ) and graph neural nets. ...
arXiv:2106.09553v3
fatcat:k7nd6jd56ffzjllyzup5fnddha
Defense Advanced Research Projects Agency (Darpa) Fiscal Year 2015 Budget Estimates
2014
Zenodo
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warriors with post-traumatic brain injury, stress, or loss of memory (i.e. the Restoring Active Memory (RAM) funding opportunity), as well as new neurotechnology-based capabilities (e.g. the Systems-Based ...
An emphasis in the FY 2015 budget request focuses on research to tailor treatments to patients' unique characteristics, known as "precision medicine." ...
FY 2013 Accomplishments: -Designed "deep-learning" neural networks for machine learning applications such as database search, medical diagnosis, motion tracking, and voice and image recognition based on ...
doi:10.5281/zenodo.1215345
fatcat:fjzhmynqjbaafk67q2ckcblj2m
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