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Möller Dietmar P.F. Guide to Cybersecurity in Digital Transformation: Trends, Methods, Technologies, Applications and Best Practices

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Möller Dietmar P.F. Guide to Cybersecurity in Digital Transformation: Trends, Methods, Technologies, Applications and Best Practices
Springer, 2023. — 361 p. — (Advances in Information Security 103). — ISBN 978-3-031-26844-1.
In today’s digital transformation environments, a rigorous cybersecurity approach to effective risk management — including contingency planning, outlining immediate actions, preparing post-breach responses — is central to defending organizations’ interconnected computer systems, networks, and infrastructure resources from malicious cyber-attacks.Specifically, cybersecurity technologies, processes, and practices need to be generalized and applied to intrusion detection and prevention measures. This entails analyzing profiles of cyber-attackers and building cyber-attack models for behavior simulation that can effectively counter such attacks. This comprehensive volume aims to cover all essential aspects of cybersecurity in digital transformation and to provide a framework for considering the many objectives and requirements involved. In addition to introducing theoretical foundations, the work also offers practical techniques for defending against malicious cybercriminals.Gathering all the relevant information, this practical guide is eminently suitable as a self-study resource for engineers, scientists, computer scientists, and chief information officers. Further, with its many examples of best practices, it can serve as an excellent text for graduate-level courses and research into cybersecurity.
Cybersecurity in Digital Transformation
Threats and Threat Intelligence
Intrusion Detection and Prevention
Cyberattacker Profiles, Cyberattack Models and Scenarios, and Cybersecurity Ontology
NIST Cybersecurity Framework and MITRE Cybersecurity Criteria
Ransomware Attacks and Scenarios: Cost Factors and Loss of Reputation
Cybersecurity Maturity Models and SWOT Analysis
Machine Learning and Deep Learning
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