Security

Dataset used in paper "Machine Learning Cryptanalysis of a Quantum Random Number Generator" published at IEEE TIFS.

 

  • Computational Intelligence
  • Last Updated On: 
    Mon, 05/13/2019 - 20:16
    Citation Author(s): 
    Nhan Duy Truong, Jing Yan Haw

    The two dataset files contains the experimental results for ICDB DMode and ICDB AMode.

  • Security
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Jyh-haw Yeh, Hung-Min Sun, Ujwal Karki, Daniel Kondratyuk, Ning Shen

    A database of lips traces
    Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.

  • Biomedical and Health Sciences
  • Last Updated On: 
    Sat, 06/16/2018 - 23:18

    Costas arrays are permutation matrices that meet the added Costas condition that, when used as a frequency-hop scheme, allow at most one time-and-frequency-offset signal bin to overlap another.  Databases to various orders have been available for many years.  Here we have a database that is far more extensive than any available before it.  A very powerful and easy-to-use Windows utility with a GUI accompanies the database.

  • Smart Grid
  • Last Updated On: 
    Sun, 03/10/2019 - 23:18

    The dataset stores a random sampling distribution with cardinality of support of 4,294,967,296 (i.e., two raised to the power of thirty-two). Specifically, the source generator is fixed as a symmetric-key cryptographic function with 64-bit input and 32-bit output. A total of 17,179,869,184 (i.e., two raised to the power of thirty-four) randomly chosen inputs are used to produce the sampling distribution as the dataset. The integer-valued sampling distribution is formatted as 4,294,967,296 (i.e., two raised to the power of thirty-two) entries, and each entry occupies one byte in storage.

  • Digital signal processing
  • Last Updated On: 
    Sat, 06/16/2018 - 23:18

    This dataset is a result of my research production into machine learning in android security. The data was obtained by a process that consisted to map a binary vector of permissions used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.

    When I did my research, the datasets of malware and benign Android applications were not available, then I give to the community a part of my research results for the future works.

  • Security
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Christian Urcuqui, Andres Navarro

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