Machine Learning, Deep Learning and AI for Cybersecurity

Online Clustering of Known and Emerging Malware Families.- Applying Word Embeddings and Graph Neural Networks for Effective Malware Classification.- A Comparative Analysis of SHAP and LIME in Detecting Malicious URLs.- Comparing Balancing Techniques for Malware Classification.- Multimodal Deception and Lie Detection Using Linguistic and Acoustic Features, Deep Models, and Large Language Models.- Enhancing Dynamic Keystroke Authentication with GAN-Optimized Deep Learning Classifiers.- Selecting Representative Samples from Malware Datasets.- FLChain: Integration of Federated Learning and Blockchain for Building Unified Models for Privacy Preservation.- On the Steganographic Capacity of Selected Learning Models.- An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack.- An Empirical Analysis of Hidden Markov Models with Momentum.- Image-Based Malware Classification Using QR and Aztec Codes.- Keystroke Dynamics for User Identification.- Distinguishing Chatbot from Human.- Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network.- Temporal Analysis of Adversarial Attacks in Federated Learning.- Steganographic Capacity of Transformer Models.- Robustness of Selected Learning Models under Label Flipping Attacks.- Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks.- Quantum Computing Methods for Malware Detection.- Reducing the Surface for Adversarial Attacks in Malware Detectors.- XAI and Android Malware Models.

• Erscheinungsdatum: 09.05.2025
• Autor/Autorin: Mark Stamp
• Reihe: Mathematics and Statistics (R0)
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 29.3 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 400 Seiten
• ISBN: 9783031831577
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.