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Introduction
The advances in modern computing and the increasingly great storage of data, even more with the advent of Big Data processing and Internet of Things applications, have allowed the applying of old mathematical techniques to efficiently determine analytical models responsible for patterns identification and optimum parameters estimation with a minimum human interaction. Thus the Machine Learning (ML) was born, enabling its application for a wide range of problems, mainly when two requirements are satisfied: there is a great amount of stored data and there is enough processing power. One interesting area which ML have been applied for is Data Security, mainly regarding detection of possible attacks to data, machines and resources/services, since these kind of attacks mine the data integrity, confidentiality and availability (what is called security triad). This way, this book is intended to gather ML solutions applied to identification and mitigation of security flaws, services disruption, data tampering and other security problems or related attacks.
Objective
This book aims to provide relevant Machine Learning solutions for improvements in Data Security as a whole. Theoretical research and experimental results, through practical findings, are welcome. Researchers, students and industry professionals that work or have interest in data security can benefit with the future reading of this book, by knowing and understanding the main used ML strategies and techniques in the state of the art.
Target Audience
Researchers and professionals that work with data security in general or applied machine learning are considered the target audience of this book, although it can benefit also who works with fog/edge computing, Internet of Things and other concepts included in the topics. This book will provide insights about how ML techniques have been applied to improve data security: identifying vulnerabilities and attacks, preserving privacy, mitigating services disruption or data leakage, etc.
Recommended Topics
Supervised learning techniques for data security;
● Unsupervised learning techniques for data security;
● Privacy-preserving with machine learning;
● Machine Learning approaches for improving IoT security;
● Machine Learning applied to access control mechanisms;
● Machine Learning for identities and access management;
● Machine Learning to secure network communications;
● Machine Learning for security requirements identification;
● Threats and vulnerabilities identification through Machine Learning methods;
● Machine learning techniques to secure fog/edge computing;
● Secure and private data analytics in IoT, fog/edge and cloud scenarios;
● Authentication and authorization processes with Machine Learning
● Secure auditing and accountability with Machine Learning
● Machine Learning in anomaly detection systems
● Machine Learning for intrusion and malware detection
● Cyber physical systems security with Machine Learning
● Machine Learning and blockchain for secure processing
Submission Procedure
Researchers and practitioners are invited to submit, until December 20, 2018 , a chapter proposal containing between 1,000 and 2,000 words, clearly explaining the mission and concerns of their proposed chapter. Authors will be notified by December 25, 2018 about the acceptance status of their proposals and chapter submission guidelines for the approved. Full chapters are expected to be submitted until Feb 19, 2019 , and all interested authors must consult the guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission.
All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Machine Learning for Data Security . All manuscripts are accepted based on a double-blind peer review editorial process.
All proposals should be submitted through the eEditorial Discovery® TM online submission manager.
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the “Information Science Reference” (formerly Idea Group Reference), “Medical Information Science Reference,” “Business Science Reference,” and “Engineering Science Reference” imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit www.igi-global.com . This publication is anticipated to be released in 2020.
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