A domain-agnostic explainable framework for network attack detection across diverse traffic datasets. [PDF]
Sci RepAlanazi A.
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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
Vision transformer framework for host based cryptojacking malware detection. [PDF]
Sci RepEl-Shafai W, Azar AT, Alshathri S.
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HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. [PDF]
PLoS OneMinh Manh V, Do Xuan C, Van NTK.
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RNN-based detection of IoT malware using diverse feature engineering methods. [PDF]
Sci RepAbd-Ellah MK +3 more
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OmBNNet: a resource-efficient FPGA-based obfuscated malware detection method using binarized neural network. [PDF]
Sci RepDas K +3 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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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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A deep learning-based IoT malware detection approach for electric vehicle charging stations. [PDF]
Sci RepXia L, Chen Y, Han L.
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Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]
Sci RepAbd-Ellah MK +3 more
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