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
Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]
Sci RepAbd-Ellah MK +3 more
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Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]
Sci RepTawfik M +6 more
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Self-Organizing Neural Grove for Malware Detection in IoT Edge Devices. [PDF]
Sensors (Basel)Inoue H, Komura T, Hashimoto I.
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A dataset of windows malware execution traces. [PDF]
Data BriefRaducu R +3 more
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A multidimensional cybercrime annotation dataset for a closed-access Russian-English underground hacking forum. [PDF]
Data BriefMischinger M +2 more
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Malware diffusion models for modern complex networks : theory and applications /
Malware Diffusion Models for Wireless Complex Networks: Theory and Applications provides a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators. As the proliferation of
Karyotis, Vasileios,author. +1 more
core
Federated ConvNeXt-swin temporal fusion network for malware and botnet detection in IoT systems. [PDF]
Sci RepAlsubaei FS, Almazroi AA, Ayub N.
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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