Malware Classification Using a Hybrid Hidden Markov Model-Convolutional Neural Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00385455" target="_blank" >RIV/68407700:21240/25:00385455 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-83157-7_4" target="_blank" >https://doi.org/10.1007/978-3-031-83157-7_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-83157-7_4" target="_blank" >10.1007/978-3-031-83157-7_4</a>
Alternative languages
Result language
angličtina
Original language name
Malware Classification Using a Hybrid Hidden Markov Model-Convolutional Neural Network
Original language description
The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of advanced machine learning techniques. In this research, we present a novel approach based on a hybrid architecture combining features extracted using a Hidden Markov Model (HMM), with a Convolutional Neural Network (CNN) then used for malware classification. Inspired by the strong results in previous work using an HMM-Random Forest model, we propose integrating HMMs, which serve to capture sequential patterns in opcode sequences, with CNNs, which are adept at extracting hierarchical features. We demonstrate the effectiveness of our approach on the popular Malicia dataset, and we obtain superior performance, as compared to other machine learning methods—our results surpass the aforementioned HMM-Random Forest model. Our findings underscore the potential of hybrid HMM-CNN architectures in bolstering malware classification capabilities, offering several promising avenues for further research in the field of cybersecurity.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Machine Learning, Deep Learning and AI for Cybersecurity
ISBN
978-3-031-83156-0
Number of pages of the result
19
Pages from-to
93-111
Number of pages of the book
642
Publisher name
Springer Nature Switzerland AG
Place of publication
Basel
UT code for WoS chapter
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