國立高雄大學圖資館 |

登入

回首頁

Machine learning, deep learning and AI for cybersecurity

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning, deep learning and AI for cybersecurityedited by Mark Stamp, Martin Jureček.
其他作者:	Stamp, Mark.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	ix, 647 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer security.
電子資源:	https://doi.org/10.1007/978-3-031-83157-7
ISBN:	9783031831577$q(electronic bk.)

Machine learning, deep learning and AI for cybersecurity
Machine learning, deep learning and AI for cybersecurity[electronic resource] /edited by Mark Stamp, Martin Jureček. - Cham :Springer Nature Switzerland :2025. - ix, 647 p. :ill. (some col.), digital ;24 cm.

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.

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 detection and analysis, intrusion detection, spam detection, and various other subfields of cybersecurity. The book places particular attention on data driven approaches, where minimal expert domain knowledge is required. This book bridges some of the gaps that exist between deep learning/AI research and practical problems in cybersecurity. The proposed topics cover a wide range of deep learning and AI techniques, including novel frameworks and development tools enabling the audience to innovate with these cutting-edge research advancements in various security-related use cases. The book is timely since it is not common to find clearly elucidated research that applies the latest developments in AI to problems in cybersecurity.

ISBN: 9783031831577$q(electronic bk.)

Standard No.: 10.1007/978-3-031-83157-7doiSubjects--Topical Terms:

184416
Computer security.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Machine learning, deep learning and AI for cybersecurity
LDR:03606nmm a2200325 a 4500 001 678136
003 DE-He213
005 20250509130232.0
006 m d
007 cr nn 008maaau
008 251021s2025 sz s 0 eng d
020 $a 9783031831577$q(electronic bk.)
020 $a 9783031831560$q(paper)
024 7 $a 10.1007/978-3-031-83157-7 $2 doi
035 $a 978-3-031-83157-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A25
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 005.8 $2 23
090 $a QA76.9.A25 $b M149 2025
245 0 0 $a Machine learning, deep learning and AI for cybersecurity $h [electronic resource] / $c edited by Mark Stamp, Martin Jureček.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a ix, 647 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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 detection and analysis, intrusion detection, spam detection, and various other subfields of cybersecurity. The book places particular attention on data driven approaches, where minimal expert domain knowledge is required. This book bridges some of the gaps that exist between deep learning/AI research and practical problems in cybersecurity. The proposed topics cover a wide range of deep learning and AI techniques, including novel frameworks and development tools enabling the audience to innovate with these cutting-edge research advancements in various security-related use cases. The book is timely since it is not common to find clearly elucidated research that applies the latest developments in AI to problems in cybersecurity.
650 0 $a Computer security. $3 184416
650 0 $a Machine learning. $3 188639
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Security Services. $3 825729
650 2 4 $a Computer Science. $3 212513
650 2 4 $a Computer Crime. $3 833604
650 2 4 $a Privacy. $3 268519
700 1 $a Stamp, Mark. $3 234734
700 1 $a Jureček, Martin. $3 991555
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-83157-7
950 $a Mathematics and Statistics (SpringerNature-11649)