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Artificial intelligence for Cybersec...

Stamp, Mark.

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Artificial intelligence for Cybersecurity

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence for Cybersecurity/ edited by Mark Stamp ... [et al.].
其他作者:	Stamp, Mark.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xvi, 380 p. :ill. (some col.), digital ;24 cm.
內容註:	Part I: Malware-Related Topics -- Generation of Adversarial Malware and Benign Examples using Reinforcement Learning -- Auxiliary-Classifier GAN for Malware Analysis -- Assessing the Robustness of an Image-based Malware Classifier with Small Level Perturbations Techniques -- Detecting Botnets Through Deep Learning and Network Flow Analysis -- Interpretability of Machine Learning-Based Results of Malware Detection Using a Set of Rules -- Mobile Malware Detection using Consortium Blockchain -- BERT for Malware Classification -- Machine Learning for Malware Evolution Detection -- Part II: Other Security Topics -- Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-based Side-channel Analysis -- Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication -- Clickbait Detection for YouTube Videos -- Survivability Using Artificial Intelligence Assisted Cyber Risk Warning -- Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics -- Machine Learning-Based Analysis of Free-Text Keystroke Dynamic -- Free-Text Keystroke Dynamics for User Authentication.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Security measures. -
電子資源:	https://doi.org/10.1007/978-3-030-97087-1
ISBN:	9783030970871

Artificial intelligence for Cybersecurity
Artificial intelligence for Cybersecurity[electronic resource] /edited by Mark Stamp ... [et al.]. - Cham :Springer International Publishing :2022. - xvi, 380 p. :ill. (some col.), digital ;24 cm. - Advances in information security,v. 542512-2193 ;. - Advances in information security ;v. 54..

Part I: Malware-Related Topics -- Generation of Adversarial Malware and Benign Examples using Reinforcement Learning -- Auxiliary-Classifier GAN for Malware Analysis -- Assessing the Robustness of an Image-based Malware Classifier with Small Level Perturbations Techniques -- Detecting Botnets Through Deep Learning and Network Flow Analysis -- Interpretability of Machine Learning-Based Results of Malware Detection Using a Set of Rules -- Mobile Malware Detection using Consortium Blockchain -- BERT for Malware Classification -- Machine Learning for Malware Evolution Detection -- Part II: Other Security Topics -- Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-based Side-channel Analysis -- Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication -- Clickbait Detection for YouTube Videos -- Survivability Using Artificial Intelligence Assisted Cyber Risk Warning -- Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics -- Machine Learning-Based Analysis of Free-Text Keystroke Dynamic -- Free-Text Keystroke Dynamics for User Authentication.

This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It's not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.

ISBN: 9783030970871

Standard No.: 10.1007/978-3-030-97087-1doiSubjects--Topical Terms:

3595924
Artificial intelligence
--Security measures.

LC Class. No.: Q335 / .C93 2022

Dewey Class. No.: 006.3

Artificial intelligence for Cybersecurity
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520 $a This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It's not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.
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