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 "species" 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's paramount features, and the canonical sequences created in the process of Multiple Sequence Alignment (MSA) production. Due to the
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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
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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