Concise Analysis of Malware Behavior
, 2012 In recent years the popularity of the internet, the network not only providing information to the general users to browse the contents of the site, but also has some network service like e-mail, e-commerce, and social networks.
Tsai, Hung-Shiuan
core
HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. [PDF]
PLoS OneMinh Manh V, Do Xuan C, Van NTK.
europepmc +1 more source
RNN-based detection of IoT malware using diverse feature engineering methods. [PDF]
Sci RepAbd-Ellah MK +3 more
europepmc +1 more source
AI-driven adaptive adversaries and the erosion of cryptographic trust in public key systems. [PDF]
J Anal Sci TechnolRadanliev P.
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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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Malware analysis and reverse engineering
, 2014 Focus of this thesis is reverse engineering in information technology closely linked with the malware analysis. It explains fundamentals of IA-32 processors architecture and basics of operating system Microsoft Windows.
Šváb, Martin
core
Advanced behavioral malware detection: a comprehensive MLOps framework with federated learning and real-time drift detection. [PDF]
Front Artif IntellEl-Hajj M, Zeineddine MAJ.
europepmc +1 more source
A deep learning-based IoT malware detection approach for electric vehicle charging stations. [PDF]
Sci RepXia L, Chen Y, Han L.
europepmc +1 more source
Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]
Sci RepAbd-Ellah MK +3 more
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
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