Demystifying AI and ML for Cyber–Threat Intelligence
| Collectivité auteur: | |
|---|---|
| Autres auteurs: | , , , |
| Résumé: | XI, 628 p. 190 illus., 143 illus. in color. text |
| Langue: | anglais |
| Publié: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Édition: | 1st ed. 2025. |
| Collection: | Information Systems Engineering and Management,
43 |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-3-031-90723-4 |
| Format: | Électronique Livre |
Table des matières:
- 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.