Journal ArticleDOI
The individual identification method of wireless device based on dimensionality reduction and machine learning
TLDR
A RF fingerprint identification method based on dimensional reduction and machine learning is proposed as a component of intrusion detection for resolving authentication security issues and improves security protection due to the introduction of RF fingerprinting.Abstract:
The access security of wireless devices is a serious challenge in present wireless network security. Radio frequency (RF) fingerprint recognition technology as an important non-password authentication technology attracts more and more attention, because of its full use of radio frequency characteristics that cannot be imitated to achieve certification. In this paper, a RF fingerprint identification method based on dimensional reduction and machine learning is proposed as a component of intrusion detection for resolving authentication security issues. We compare three kinds of dimensional reduction methods, which are the traditional PCA, RPCA and KPCA. And we take random forests, support vector machine, artificial neural network and grey correlation analysis into consideration to make decisions on the dimensional reduction data. Finally, we obtain the recognition system with the best performance. Using a combination of RPCA and random forests, we achieve 90% classification accuracy is achieved at SNR
$$\ge $$
10 dB when reduced dimension is 76. The proposed method improves wireless device authentication and improves security protection due to the introduction of RF fingerprinting.read more
Citations
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Journal ArticleDOI
An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks
TL;DR: The experimental results indicate that the AC, FAR, and timeliness of the CNN–IDS model are higher than those of traditional algorithms, therefore, the model has not only research significance but also practical value.
Journal ArticleDOI
Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer
TL;DR: The investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL, and develops a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waves while being represented in an image data format.
Journal ArticleDOI
Multimodel Framework for Indoor Localization Under Mobile Edge Computing Environment
TL;DR: A new framework for indoor localization under mobile edge computing environment, named Multimodel, is proposed from the theoretical perspective, mainly based on the observation that the environment of the sample data collection and that of localization data collection may change seriously.
Journal ArticleDOI
Dynamic Spectrum Interaction of UAV Flight Formation Communication With Priority: A Deep Reinforcement Learning Approach
TL;DR: A combination of deep reinforcement learning (DRL) and the long-short-term memory (LSTM) network is adopted to accelerate the convergence speed of the algorithm and the quality of experience (QoE) is introduced to evaluate the results of UAV sharing.
Journal ArticleDOI
Feature selection for IoT based on maximal information coefficient
TL;DR: The results show that the proposed method achieves better performance than the comparison methods, markedly reducing feature dimensionality in order to process the tremendous quantities of data in IoT.
References
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Support vector machine active learning with applications to text classification
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