Revolutionizing Obfuscated Malware Detection Through Memory-Aware Hybrid Attention Networks (MAHAN)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50023281" target="_blank" >RIV/62690094:18450/25:50023281 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICOCO67189.2025.11334091" target="_blank" >http://dx.doi.org/10.1109/ICOCO67189.2025.11334091</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICOCO67189.2025.11334091" target="_blank" >10.1109/ICOCO67189.2025.11334091</a>
Alternative languages
Result language
angličtina
Original language name
Revolutionizing Obfuscated Malware Detection Through Memory-Aware Hybrid Attention Networks (MAHAN)
Original language description
Recently, obfuscated malware can be considered as one of the big problems for cybersecurity since many research studies were conducted to overcome it. Thus, this research presents an innovative deep learning architecture called Memory-Aware Hybrid Attention Network (MAHAN). It helps to assist researchers in finding obfuscated malware in different ways. The CIC-MalMem-2022 dataset is used in a full benchmarking analysis to compare deep learning models such as CNN, LSTM, and CNN-LSTM with traditional machine learning models such as LightGBM, Random Forest, XGBoost, K-Nearest Neighbours, Support Vector Machine, and Naive Bayes. MAHAN uses dilated convolutions, bidirectional LSTM, and multi-head attention to find complex patterns in volatile memory data which change over time and space. The experimental results show that MAHAN is competitive and either beats or matches the best models in detecting the obfuscated malware. Our findings demonstrate that memory-aware, and attention-based architectures may facilitate the detection of obfuscated malware, particularly when the malwares are not easily perceptible. © 2025 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE International Conference on Computing, ICOCO 2025
ISBN
979-8-3315-7539-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
453-458
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
New York
Event location
Kuching
Event date
Oct 6, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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