Self-Organizing Neural Grove for Malware Detection in IoT Edge Devices. [PDF]
Sensors (Basel)Inoue H, Komura T, Hashimoto I.
europepmc +1 more source
Classification of malicious code based on transformer and CNN
四川大学学报. 自然科学版Existing CNN-based malware classification methods suffer from high training costs and low accuracy for minority classes.To overcome these limitations， this paper proposes an improved method based on improved MobileVit， which combines the characteristics ...
MOU Yu-Meng +3 more
doaj
A dataset of windows malware execution traces. [PDF]
Data BriefRaducu R +3 more
europepmc +1 more source
Enhancing security in IoMT using federated TinyGAN for lightweight and accurate malware detection. [PDF]
Sci RepS D, Shankar MG, Daniel E, R BGV.
europepmc +1 more source
Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger. [PDF]
Sensors (Basel)Maghanaki M +3 more
europepmc +1 more source
Profiling and Visualizing Android Malware Datasets
Profilage et Visualisation de Datasets d’Applications Android Malveillantes Les dispositifs mobiles sont ubiquitaires: aujourd’hui la majorité des gens possèdent un téléphone mobile. A cause de ce fait, ces dispositifs sont une cible d’intérêt pour les attaquants.
openaire +2 more sources
A hierarchical deep learning framework with doubly regularized loss for robust malware detection and family categorization. [PDF]
Sci RepAbed Alsaedi S +6 more
europepmc +1 more source
Efficient feature ranked hybrid framework for android Iot malware detection. [PDF]
Sci RepSaeed NH +3 more
europepmc +1 more source
BERT-spaCy hybrid NLP and blockchain-enhanced adaptive CTI for IOC extraction and threat prediction. [PDF]
Sci RepMishra S, Alfahidah RA, Alharbi F.
europepmc +1 more source