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An Advanced Profile Hidden Markov Model for Malware Detection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424315" target="_blank" >RIV/00216208:11320/20:10424315 - isvavai.cz</a>

  • Result on the web

    <a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=-Pq5C_4REv" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=-Pq5C_4REv</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3233/IDA-194639" target="_blank" >10.3233/IDA-194639</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    An Advanced Profile Hidden Markov Model for Malware Detection

  • Original language description

    The rapid growth of malicious software (malware) production in recent decades and the increasing number of threats posed by malware to network environments, such as the Internet and intelligent environments, emphasize the need for more research on the security of computer networks in information security and digital forensics. The method presented in this study identifies &quot;species&quot; of malware families, which are more sophisticated, obfuscated, and structurally diverse. We propose a hybrid technique combining aspects of signature detection with machine learning-based methods to classify malware families. The method is carried out by utilizing Profile Hidden Markov Models (PHMMs) on the behavioral characteristics of malware species. This paper explains the process of modeling and training an advanced PHMM using sequences obtained from the extraction of each malware family&apos;s paramount features, and the canonical sequences created in the process of Multiple Sequence Alignment (MSA) production. Due to the

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/EF16_027%2F0008495" target="_blank" >EF16_027/0008495: International Mobility of Researchers at Charles University</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Intelligent Data Analysis

  • ISSN

    1088-467X

  • e-ISSN

  • Volume of the periodical

    24

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    20

  • Pages from-to

    759-778

  • UT code for WoS article

    000551095000002

  • EID of the result in the Scopus database

    2-s2.0-85089309653