WSN malware infection model based on cellular automaton and static Bayesian game
The theoretical model for the malware infection in wireless sensor networks (WSN) based on cellular automaton and static Bayesian game was studied.Firstly,the malware infection model of WSN based on cellular automaton was built.Secondly,the malware ...
Hong ZHANG +3 more
doaj
Malware and Anti-Malware: A Comprehensive Review
With the explosive growth of digital connectivity and the professionalization of cybercrime, malware attacks have become one of the most persistent and sophisticated cybersecurity threats. This landscape now ranges from traditional viruses and worms to complex ransomware-as-a-service (RaaS) syndicates and advanced persistent threats (APTs).
openaire +1 more source
HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. [PDF]
Minh Manh V, Do Xuan C, Van NTK.
europepmc +1 more source
AI-driven adaptive adversaries and the erosion of cryptographic trust in public key systems. [PDF]
Radanliev P.
europepmc +1 more source
RNN-based detection of IoT malware using diverse feature engineering methods. [PDF]
Abd-Ellah MK +3 more
europepmc +1 more source
OmBNNet: a resource-efficient FPGA-based obfuscated malware detection method using binarized neural network. [PDF]
Das K +3 more
europepmc +1 more source
A deep learning-based IoT malware detection approach for electric vehicle charging stations. [PDF]
Xia L, Chen Y, Han L.
europepmc +1 more source
Advanced behavioral malware detection: a comprehensive MLOps framework with federated learning and real-time drift detection. [PDF]
El-Hajj M, Zeineddine MAJ.
europepmc +1 more source
Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]
Abd-Ellah MK +3 more
europepmc +1 more source
Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]
Tawfik M +5 more
europepmc +1 more source

