Demystifying AI and ML for Cyber-Threat Intelligence

A Comprehensive Review on the Detection Capabilities of IDS using Deep Learning Techniques.- Next-Generation Intrusion Detection Framework with Active Learning-Driven Neural Networks for DDoS Defense.- Ensemble Learning-based Intrusion Detection System for RPL-based IoT Networks.- Advancing Detection of Man-in-the-Middle Attacks through Possibilistic C-Means Clustering.- CNN-Based IDS for Internet of Vehicles Using Transfer Learning.- Real-Time Network Intrusion Detection System using Machine Learning.- OpIDS-DL : OPTIMIZING INTRUSION DETECTION IN IoT NETWORKS: A DEEP LEARNING APPROACH WITH REGULARIZATION AND DROPOUT FOR ENHANCED CYBERSECURITY.- ML-Powered Sensitive Data Loss Prevention Firewall for Generative AI Applications.- Enhancing Data Integrity: Unveiling the Potential of Reversible Logic for Error Detection and Correction.- Enhancing Cyber security through Reversible Logic.- Beyond Passwords: Enhancing Security with Continuous Behavioral Biometrics and Passive Authentication.

• Erscheinungsdatum: 16.08.2025
• Autor/Autorin: Ming Yang
• Reihe: Intelligent Technologies and Robotics (R0)
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 30.7 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 350 Seiten
• ISBN: 9783031907234
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.