Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
- 5h 10m
- Anacleto Correia, Victor Lobo
- IGI Global
- 2023
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.
About the Author
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.
In this Book
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User Profiling Using Keystroke Dynamics and Rotation Forest
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Predictive Modelling for Financial Fraud Detection Using Data Analytics—A Gradient-Boosting Decision Tree
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Comprehensive Overview of Autonomous Vehicles and Their Security against DDoS Attacks
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Application of Machine Learning to User Behavior-Based Authentication in Smartphone and Web
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The Role of Deception in Securing Our Cyberspace—Honeypots are a Viable Option
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Holistic View on Detecting DDoS Attacks Using Machine Learning
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Masked Transient Effect Ring Oscillator Physical Unclonable Function against Machine Learning Attacks
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Detecting Bank Financial Fraud in South Africa Using a Logistic Model Tree
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Innovative Legitimate Non-Traditional Doctorate Programs in Cybersecurity, Engineering, and Technology
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Privacy Preservation of Image Data with Machine Learning
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Compilation of References
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Related References
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