Cyber Security Meets Machine Learning / edited by Xiaofeng Chen, Willy Susilo, Elisa Bertino.

Format:

Book

Contributor:

Chen, Xiaofeng, Editor.

Susilo, Willy, Editor.

Bertino, Elisa, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Data protection.

Machine learning.

Image processing-Digital techniques.

Computer vision.

Database management.

Computer networks.

Application software.

Data and Information Security.

Machine Learning.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Database Management System.

Computer Communication Networks.

Computer and Information Systems Applications.

Local Subjects:

Data and Information Security.

Machine Learning.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Database Management System.

Computer Communication Networks.

Computer and Information Systems Applications.

Physical Description:

1 online resource (IX, 163 pages) : 41 illustrations, 24 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Other Title:

ss

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

Contents:

Chapter 1. IoT Attacks and Malware

Chapter 2. Machine Learning-based Online Source Identification for Image Forensics

Chapter 3. Reinforcement Learning Based Communication Security for Unmanned Aerial Vehicles

Chapter 4. Visual Analysis of Adversarial Examples in Machine Learning

Chapter 5. Adversarial Attacks against Deep Learning-based Speech Recognition Systems

Chapter 6. Secure Outsourced Machine Learning

Chapter 7. A Survey on Secure Outsourced Deep Learning.

Other Format:

Printed edition:

ISBN:

978-981-33-6726-5

9789813367265

Access Restriction:

Restricted for use by site license.