Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity

Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity

Indexed In: SCOPUS
Release Date: June, 2022|Copyright: © 2022 |Pages: 271
DOI: 10.4018/978-1-7998-9430-8
ISBN13: 9781799894308|ISBN10: 1799894304|ISBN13 Softcover: 9781799894315|EISBN13: 9781799894322
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Description & Coverage
Description:

The growth of innovative cyber threats, many based on metamorphosing techniques, has led to security breaches and the exposure of critical information in sites that were thought to be impenetrable. The consequences of these hacking actions were, inevitably, privacy violation, data corruption, or information leaking. Machine learning and data mining techniques have significant applications in the domains of privacy protection and cybersecurity, including intrusion detection, authentication, and website defacement detection, that can help to combat these breaches.

Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity provides machine and deep learning methods for analysis and characterization of events regarding privacy and anomaly detection as well as for establishing predictive models for cyber attacks or privacy violations. It provides case studies of the use of these techniques and discusses the expected future developments on privacy and cybersecurity applications. Covering topics such as behavior-based authentication, machine learning attacks, and privacy preservation, this book is a crucial resource for IT specialists, computer engineers, industry professionals, privacy specialists, security professionals, consultants, researchers, academicians, and students and educators of higher education.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Autonomous Vehicles
  • Behavior-Based Authentication
  • Cyber Physical Systems
  • Data Analytics
  • Financial Fraud Detection
  • Image Data
  • Keystroke Dynamics
  • Machine Learning Attacks
  • Phishing Detection
  • Predictive Modelling
  • Privacy Preservation
  • User Profiling
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Editor/Author Biographies

Victor Lobo is an Invited Full Professor, NOVA Information Management School (NOVA IMS).

Anacleto Correia (M) is an Associate Professor and lecturer of Management and Information Systems subjects at the Portuguese Navy Academy. He holds a Ph.D. in Computer Science, an M.Sc. in Statistics and Information Management, a B.Sc. degree in Management, and also a B.Sc. at Portuguese Naval Academy. His research interests are focused on requirements engineering, software engineering, process modeling, data mining, machine learning, and business engineering. He has also more than 20 years of experience in industry-leading projects and architecting large software development projects and is the author of dozens of scientific papers in journals and conference proceedings.

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