Printed from https://ideas.repec.org/b/spr/sprbok/978-3-031-83157-7.html
Machine Learning, Deep Learning and AI for Cybersecurity
Editor
Listed:
- Mark Stamp(San José State University, Department of Computer Science)Martin Jureček(Czech Technical University in Prague, Department of Information Security)
Abstract
No abstract is available for this item.
Individual chapters are listed in the "Chapters" tab
Suggested Citation
DOI: 10.1007/978-3-031-83157-7
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Book Chapters
The following chapters of this book are listed in IDEAS- Atharva Khadilkar & Mark Stamp, 2025. "Image-Based Malware Classification Using QR and Aztec Codes," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 3-35, Springer.
- Olha Jurečková & Martin Jureček & Mark Stamp, 2025. "Online Clustering of Known and Emerging Malware Families," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 37-59, Springer.
- Ranjit John & Fabio Di Troia, 2025. "Comparing Balancing Techniques for Malware Classification," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 61-92, Springer.
- Ritik Mehta & Olha Jurečková & Mark Stamp, 2025. "Malware Classification Using a Hybrid Hidden Markov Model-Convolutional Neural Network," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 93-111, Springer.
- Lukáš Děd & Martin Jureček, 2025. "Selecting Representative Samples from Malware Datasets," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 113-142, Springer.
- Manasa Mananjaya & Fabio Di Troia, 2025. "Applying Word Embeddings and Graph Neural Networks for Effective Malware Classification," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 143-167, Springer.
- Andrew Miller & Fabio Di Troia & Mark Stamp, 2025. "An Empirical Analysis of Hidden Markov Models with Momentum," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 169-206, Springer.
- Eliška Krátká & Aurél Gábor Gábris, 2025. "Quantum Computing Methods for Malware Detection," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 207-228, Springer.
- Benjamín Peraus & Martin Jureček, 2025. "Reducing the Surface for Adversarial Attacks in Malware Detectors," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 231-266, Springer.
- Matouš Kozák & Martin Jureček, 2025. "Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 267-290, Springer.
- Ayush Nair & Fabio Di Troia, 2025. "A Comparative Analysis of SHAP and LIME in Detecting Malicious URLs," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 291-325, Springer.
- Maithili Kulkarni & Mark Stamp, 2025. "XAI and Android Malware Models," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 327-355, Springer.
- Rohit Mapakshi & Sayma Akther & Mark Stamp, 2025. "Temporal Analysis of Adversarial Attacks in Federated Learning," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 359-392, Springer.
- K. M. Sameera & Dincy R. Arikkat & P. Vinod & Rehiman K. A. Rafidha & Azin Aneez & Mauro Conti, 2025. "Federated Learning: An Overview of Attacks and Defense Methods," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 393-431, Springer.
- Kunal Bhatnagar & Sagana Chattanathan & Angela Dang & Bhargav Eranki & Ronnit Rana & Charan Sridhar & Siddharth Vedam & Angie Yao & Mark Stamp, 2025. "An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 433-454, Springer.
- Rishit Agrawal & Kelvin Jou & Tanush Obili & Daksh Parikh & Samarth Prajapati & Yash Seth & Charan Sridhar & Nathan Zhang & Mark Stamp, 2025. "On the Steganographic Capacity of Selected Learning Models," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 457-491, Springer.
- Sarvagya Bhargava & Mark Stamp, 2025. "Robustness of Selected Learning Models Under Label-Flipping Attack," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 493-506, Springer.
- Lei Zhang & Dong Li & Olha Jurečková & Mark Stamp, 2025. "Steganographic Capacity of Transformer Models," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 507-526, Springer.
- Gauri Anil Godghase & Rishit Agrawal & Tanush Obili & Mark Stamp, 2025. "Distinguishing Chatbot from Human," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 529-564, Springer.
- Tien Nguyen & Faranak Abri & Akbar Siami Namin & Keith S. Jones, 2025. "Multimodal Deception Detection Using Linguistic and Acoustic Features," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 565-598, Springer.
- Atharva Sharma & Martin Jureček & Mark Stamp, 2025. "Keystroke Dynamics for User Identification," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 601-622, Springer.
- Jonathan A. Bazan & Katerina Potika & Petros Potikas, 2025. "Enhancing Free Text Keystroke Authentication with GAN-Optimized Deep Learning Classifiers," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 623-647, Springer.
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