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Machine Learning, Deep Learning and AI for Cybersecurity

This book addresses a variety of problems that arise at the interface between AI techniques and challenging problems in cybersecurity. The book covers many of the issues that arise when applying AI and deep learning algorithms to inherently difficult problems in the security domain, such as malware...

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Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Stamp, Mark (Editor), Jureček, Martin (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Udgivelse:1st ed. 2025.
Fag:
Machine learning.
Artificial intelligence.
Data protection.
Computer science.
Computer crimes.
Data protection > Law and legislation.
Machine Learning.
Artificial Intelligence.
Security Services.
Computer Science.
Computer Crime.
Privacy.
Online adgang:https://doi.org/10.1007/978-3-031-83157-7
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Indholdsfortegnelse:
  • 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.

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