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  • 1
    Online Resource
    Online Resource
    Encrypted 5G over-the-top voice traffic classification using deep learning
    International Telecommunication Union ; 2022
    In:  ITU Journal on Future and Evolving Technologies Vol. 3, No. 3 ( 2022-12-09), p. 779-792
    Details
    In: ITU Journal on Future and Evolving Technologies, International Telecommunication Union, Vol. 3, No. 3 ( 2022-12-09), p. 779-792
    Abstract: With the commercialization of fifth-generation (5G), the rapid popularity of mobile Over-The-Top (OTT) voice applications brings huge impacts on the traditional telecommunication voice call services. Tunnel encryption technology such as Virtual Private Networks (VPNs) allow OTT users to escape the supervision of network operators easily, which may cause potential security risks to cyberspace. To monitor harmful OTT applications in the context of 5G, it is critical to identify encrypted OTT voice traffic. However, there is no comprehensive study on typical OTT voice traffic identification. This paper mainly focuses on analyzing OTT voice traffic in the 5G network specifically. We propose employing Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) to identify encrypted 5G OTT voice traffic, study the identification performance of used deep learning methods in three different scenarios. To verify the performance of the proposed approach, we collect 28 types of typical OTT and non-OTT voice traffic from the experimental 5G network. Experimental results prove the effectiveness and robustness of the proposed approach in encrypted 5G OTT voice traffic identification.
    Type of Medium: Online Resource
    ISSN: 2616-8375
    URL: Article
    DOI: 10.52953/EYIF3681
    Language: English
    Publisher: International Telecommunication Union
    Publication Date: 2022
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Biological and therapeutic role of LSD1 in Alzheimer’s diseases
    Frontiers Media SA ; 2022
    In:  Frontiers in Pharmacology Vol. 13 ( 2022-10-25)
    Details
    In: Frontiers in Pharmacology, Frontiers Media SA, Vol. 13 ( 2022-10-25)
    Abstract: Alzheimer’s disease (AD) is a common chronic neurodegenerative disease characterized by cognitive learning and memory impairments, however, current treatments only provide symptomatic relief. Lysine-specific demethylase 1 (LSD1), regulating the homeostasis of histone methylation, plays an important role in the pathogenesis of many neurodegenerative disorders. LSD1 functions in regulating gene expression via transcriptional repression or activation, and is involved in initiation and progression of AD. Pharmacological inhibition of LSD1 has shown promising therapeutic benefits for AD treatment. In this review, we attempt to elaborate on the role of LSD1 in some aspects of AD including neuroinflammation, autophagy, neurotransmitters, ferroptosis, tau protein, as well as LSD1 inhibitors under clinical assessments for AD treatment.
    Type of Medium: Online Resource
    ISSN: 1663-9812
    URL: Article
    DOI: 10.3389/fphar.2022.1020556
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2587355-6
    SSG: 15,3
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  • 3
    Online Resource
    Online Resource
    Modulation Type Recognition Algorithm Based on Modulation Instantaneous Structure Difference and Deep Learning
    IOP Publishing ; 2021
    In:  IOP Conference Series: Earth and Environmental Science Vol. 692, No. 4 ( 2021-03-01), p. 042073-
    Details
    In: IOP Conference Series: Earth and Environmental Science, IOP Publishing, Vol. 692, No. 4 ( 2021-03-01), p. 042073-
    Abstract: In order to solve the problems of signal modulation recognition in non-cooperative communication, this paper proposes a modulation type recognition algorithm based on instantaneous difference by neural network. Firstly, the method uses the structural difference of modulation parameters in time domain of modulation signal, and displays the difference in the form of image, so as to transform the modulation recognition problem into image recognition problem; secondly, it uses the advantage of convolution neural network to automatically extract features, and it classify different modulation signals; finally, a hierarchical neural network structure is formed to identify the unknown modulation signals.
    Type of Medium: Online Resource
    ISSN: 1755-1307 , 1755-1315
    URL: Article
    DOI: 10.1088/1755-1315/692/4/042073
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2434538-6
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  • 4
    Online Resource
    Online Resource
    A Blind Recognition Algorithm of Scrambler after Convolutional Encoder
    IOP Publishing ; 2021
    In:  Journal of Physics: Conference Series Vol. 1944, No. 1 ( 2021-06-01), p. 012010-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1944, No. 1 ( 2021-06-01), p. 012010-
    Abstract: In a wireless communication system, the data is usually scrambled after channel encoding. However, existing research on blind recognition often ignores the pseudo-random scrambling of channel encoding data, which is not in line with the actual situation. To solve this problem, this paper proposes an algorithm to recognize the scrambler parameters with convolutional codes. First, we transform this problem into a cognition problem of cancelling scrambling sequence by using the properties of the polynomial of scrambler. Then, we put forward a fast judgment method after the cancellation of scrambling. The proposed method is based on the conditional entropy and can effectively identify the scrambler after the convolutional encoder. The method has a better performance of resisting error comparing to the traditional method and saves computing resources. Simulation results show that the parameters of the scrambler can still be determined effectively when the bit error rate is 6% in the case of sufficient data.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1944/1/012010
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2166409-2
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