Malware Classification Using a Hybrid Hidden Markov Model-Convolutional Neural Network

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Malware Classification Using a Hybrid Hidden Markov Model-Convolutional Neural Network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Malware Classification Using a Hybrid Hidden Markov Model-Convolutional Neural Network

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Machine Learning, Deep Learning and AI for Cybersecurity

ISBN

978-3-031-83156-0

Počet stran výsledku

19

Strana od-do

93-111

Počet stran knihy

642

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Basel

Kód UT WoS kapitoly

—