Machine Learning for Cybersecurity

"Machine Learning for Cybersecurity: Threat Detection and Mitigation" delves into the transformative role of machine learning in addressing contemporary cybersecurity challenges. This reprint provides an in-depth exploration of how advanced techniques such as deep learning, natural language processi...

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Xehetasun bibliografikoak
Formatua:	Online
Hizkuntza:	ingelesa
Argitaratua:	MDPI - Multidisciplinary Digital Publishing Institute 2025
Gaiak:	generative adversarial networks data anonymization privacy preservation loss feedback feature coding power data protection Internet of Things denial-of-service attack Information-Centric Network machine learning network intrusion detection datasets engineering deep learning feature fusion cybersecurity oversampling technique undersampling technique multi-class classification vulnerability mining hotspot code fuzzing AFL device fingerprinting neuromorphic spiking neural network eventization encoding SNN operational technology OT random forest WirelessHART artificial intelligence cyber threat intelligence cyber resilience ethical considerations CTI and AI biases anomaly detection DCNN Internet of Things (IoT) machine learning (ML) SVM XGBoost security blockchain ELF static analysis binary lifting opcode sequence analysis malware detection malware classification fake account detection Twitter (X) image classification syntax aware protocol implementations large language models Linux system calls quantum computing IoMT post-quantum lateral movement threat mitigation unsupervised learning attack graphs active directory hardening placement robotics security industrial control systems network-based intrusion detection systems thema EDItEUR::A The Arts::AT Performing arts::ATF Films, cinema thema EDItEUR::A The Arts::AT Performing arts::ATJ Television
Sarrera elektronikoa:	ONIX_20250220_9783725827947_435
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ONIX_20250220_9783725827947_435

Machine Learning for Cybersecurity

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