The Experts below are selected from a list of 16650 Experts worldwide ranked by ideXlab platform

Zago, Mendeley M Data) - One of the best experts on this subject based on the ideXlab platform.

  • ReCAN Data - Reverse engineering of Controller Area Networks
    2020
    Co-Authors: Zago, Mendeley M Data)
    Abstract:

    Abstract --------- This article details the methodology and the approach used to extract and deCode the data obtained from the Controller Area Network (CAN) buses in three personal vehicles and four commercial trucks. The dataset is composed of two complementary parts, namely the raw data and the deCoded ones. Along with the description of the data, this article also reports both hardware and software requirements to firstly extract the data from the vehicles and secondly deCode the binary data frames to obtain the actual sensors' data. Finally, necessary Code snippets have been described in pseudo-Code and will be publicly available in a Code Repository. Preliminary results suggest that motivated enough actors may intercept, interact and recognize the vehicle data with consumer-grade technology, ultimately refuting, once-again, the security-through-obscurity paradigm used by automotive manufacturer as main defensive countermeasure. Keywords ---------- Automotive; Controller Area Network (CAN); Reverse Engineering; Dataset Type of data ------------- - RAW: CSV files with timestamp, CANline, ECU identifier, binary data - DeCoded: CSV files with timestamp, CANline, ECU identifier, variable, value How data where acquired ---------------------------- Controller Area Network (CAN) buses have been accessed using a standard CAN connector and a CANtact board. The CAN Utils library, publicly available in the Linux Kernel, has been used to intercept the network traffic of the vehicle. Sensors data have been deCoded using state-of-the-art algorithm. Source Code for each step of the analysis is publicly available in the Repository, as specified below. Parameters: - Cars: 500k baudrate, connected o the OBD-II port of each vehicle. - Trucks: 500k baudrate, connected both to the OBD-II port and to a second wire into a second CAN bus. Source Code ------------- - Repository: TBA - Identification number: TBA - URL: TBA Acknowledgments -------------------- This study was founded by a predoctoral grant from the Spanish National Cybersecurity Institute (INCIBE) within the program "Ayudas para la Excelencia de los Equipos de Investigación Avanzada en Ciberseguridad" ("Grants for the Excellence of Advanced Cybersecurity Research Teams"), with Code INCIBEI-2015-27353; a predoctoral travel grant within the program "Ayudas para estancias en el estranjero de alumnos de doctorado en las líneas de actuación de Campus Mare Nostrum" ("Grants for stays abroad of Ph.D. students within the lines of action of Campus Mare Nostrum'')

  • ReCAN Data - Reverse engineering of Controller Area Networks
    2020
    Co-Authors: Zago, Mendeley M Data)
    Abstract:

    Abstract --------- This article details the methodology and the approach used to extract and deCode the data obtained from the Controller Area Network (CAN) buses in three personal vehicles and four commercial trucks. The dataset is composed of two complementary parts, namely the raw data and the deCoded ones. Along with the description of the data, this article also reports both hardware and software requirements to firstly extract the data from the vehicles and secondly deCode the binary data frames to obtain the actual sensors' data. Finally, necessary Code snippets have been described in pseudo-Code and will be publicly available in a Code Repository. Preliminary results suggest that motivated enough actors may intercept, interact and recognize the vehicle data with consumer-grade technology, ultimately refuting, once-again, the security-through-obscurity paradigm used by automotive manufacturer as main defensive countermeasure. Keywords ---------- Automotive; Controller Area Network (CAN); Reverse Engineering; Dataset Type of data ------------- - RAW: CSV files with timestamp, CANline, ECU identifier, binary data - DeCoded: CSV files with timestamp, CANline, ECU identifier, variable, value How data where acquired ---------------------------- Controller Area Network (CAN) buses have been accessed using a standard CAN connector and a CANtact board. The CAN Utils library, publicly available in the Linux Kernel, has been used to intercept the network traffic of the vehicle. Sensors data have been deCoded using state-of-the-art algorithm. Source Code for each step of the analysis is publicly available in the Repository, as specified below. Parameters: - Cars: 500k baudrate, connected o the OBD-II port of each vehicle. - Trucks: 500k baudrate, connected both to the OBD-II port and to a second wire into a second CAN bus. Source Code ------------- - Repository: ReCAN Source - Reverse engineering of Controller Area Networks - Provider: Github - Identification number: 10.5281/zenodo.3625715 - URL: https://github.com/Cyberdefence-Lab-Murcia/ReCAN Acknowledgments -------------------- This study was founded by a predoctoral grant from the Spanish National Cybersecurity Institute (INCIBE) within the program "Ayudas para la Excelencia de los Equipos de Investigación Avanzada en Ciberseguridad" ("Grants for the Excellence of Advanced Cybersecurity Research Teams"), with Code INCIBEI-2015-27353; a predoctoral travel grant within the program "Ayudas para estancias en el estranjero de alumnos de doctorado en las líneas de actuación de Campus Mare Nostrum" ("Grants for stays abroad of Ph.D. students within the lines of action of Campus Mare Nostrum'')

Underwood Ted - One of the best experts on this subject based on the ideXlab platform.

  • Wordcount Data for Chapter 3 of Distant Horizons
    2018
    Co-Authors: Underwood Ted
    Abstract:

    Data to support calculations in chapter 3 of the book _Distant Horizons._ It includes word counts for volumes of poetry and fiction, used to study the boundary between reviewed and relatively obscure writing. For the argument founded on this data, see the book and the supporting Code Repository: https://github.com/tedunderwood/horizon/tree/master/chapter3.Ope

  • Wordcount Data for Chapter 2 of Distant Horizons
    2018
    Co-Authors: Underwood Ted
    Abstract:

    Data to support calculations in chapter 2 of the book _Distant Horizons._ It includes word counts for volumes of fiction, especially Gothic, detective, and science fiction from the nineteenth and twentieth centuries. For the argument about genre founded on this data, see the book and the supporting Code Repository: https://github.com/tedunderwood/horizon/tree/master/chapter2.Ope

  • Data for Chapter 4 of Distant Horizons
    2018
    Co-Authors: Underwood Ted
    Abstract:

    Data to support chapter 4 of the book _Distant Horizons._ This includes mostly lists of words associated with specific literary characters. The file 4a will unpack into a folder of ~80,000 separate .tsv files, one for each character; the files 4b, 4c, and 4d unpack into a smaller number of larger files that aggregate characters. 4a is in effect a subset of 4b, c, and d, though it is formatted differently. For the argument based on this data, consult the book Distant Horizons, and the supporting Code Repository: https://github.com/tedunderwood/horizon/tree/master/chapter4.Ope

Abdulrahman Alatawi - One of the best experts on this subject based on the ideXlab platform.

  • Statistical Unigram Analysis for Source Code Repository
    International Journal of Semantic Computing, 2018
    Co-Authors: Abdulrahman Alatawi, Omar El Ariss, Yunkai Liu
    Abstract:

    Unigram is a fundamental element of n-gram in natural language processing. However, unigrams collected from a natural language corpus are unsuitable for solving problems in the domain of computer p...

  • statistical unigram analysis for source Code Repository
    IEEE International Conference on Multimedia Big Data, 2017
    Co-Authors: Omar El Ariss, Yunkai Liu, Abdulrahman Alatawi
    Abstract:

    Unigram is a fundamental element of n-gram in natural language processing. However, unigrams collected from a natural language corpus are unsuitable for solving problems in the domain of computer programming languages. In this paper, we analyze the properties of unigrams collected from an ultra-large source Code Repository. Specifically, we have collected 1.01 billion unigrams from 0.7 million open source projects hosted at GitHub.com. By analyzing these unigrams, we have discovered statistical patterns regarding (1) how developers name variables, methods, and classes, and (2) how developers choose abbreviations. Our study describes a probabilistic model for solving a well-known problem in source Code analysis: how to expand a given abbreviation to its original indented word. It shows that the unigrams collected from source Code repositories are essential resources to solving the domain specific problems.

Mikhail V Gorshkov - One of the best experts on this subject based on the ideXlab platform.

  • pyteomics a python framework for exploratory data analysis and rapid software prototyping in proteomics
    Journal of the American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the source Code Repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Anton Goloborodko - One of the best experts on this subject based on the ideXlab platform.

  • pyteomics a python framework for exploratory data analysis and rapid software prototyping in proteomics
    Journal of the American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the source Code Repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com