Results 81 to 90 of about 36,905 (167)

Advanced behavioral malware detection: a comprehensive MLOps framework with federated learning and real-time drift detection

Frontiers in Artificial Intelligence
This paper presents a comprehensive MLOps framework for behavioral malware detection that addresses critical challenges in generalization, collaboration, and operational resilience.
Mohammed El-Hajj, Mohammad Al Jawad Zeineddine   +1 more
doaj   +1 more source

Microarchitectural Malware Detection via Translation Lookaside Buffer (TLB) Events

Journal of Cybersecurity and Privacy
Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification.
Cristian Agredo, Daniel F. Koranek, Christine M. Schubert Kabban, Jose A. Gutierrez del Arroyo, Scott R. Graham   +4 more
doaj   +1 more source

EDSSR: a secure and power-aware opportunistic routing scheme for WSNs

Scientific Reports
Motivated by the pivotal role of routing in Wireless Sensor Networks (WSNs) and the prevalent security vulnerabilities arising from existing protocols, this research tackles the inherent challenges of securing WSNs.
Ruili Yang, Manoj A. Patil, Potu Narayana, D. Jayaram, K. Keerthi, N. SudhakarYadav, Premkumar Chithaluru, Sunil Kumar, Diaa Salama Abd Elminaam, Deema Mohammed Alsekait   +9 more
doaj   +1 more source

A deep learning-based IoT malware detection approach for electric vehicle charging stations. [PDF]

Sci Rep
Xia L, Chen Y, Han L.
europepmc   +1 more source

Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]

Sci Rep
Abd-Ellah MK, Alsayed NA, Elkomy OM, El-Hady WM.   +3 more
europepmc   +1 more source

Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]

Sci Rep
Tawfik M, Tarazi H, Dalalah A, Bajeszeyadaljunaeidia, Shaban WM, Fathi IS.   +5 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, Keramati S, Chen FF, Shahin M.   +3 more
europepmc   +1 more source

Enhancing security in IoMT using federated TinyGAN for lightweight and accurate malware detection. [PDF]

Sci Rep
S D, Shankar MG, Daniel E, R BGV.
europepmc   +1 more source

Hierarchical malware detection, family identification, and variant attribution using CNN-based hybrid models on grayscale executable images. [PDF]

Sci Rep
Saxena M, Das T.
europepmc   +1 more source

Efficient feature ranked hybrid framework for android Iot malware detection. [PDF]

Sci Rep
Saeed NH, Hamza AA, Sobh MA, Bahaa-Eldin AM.   +3 more
europepmc   +1 more source