Jahankhani H., Bowen G., Sharif M.S., Hussien O. (eds.) Cybersecurity and Artificial Intelligence: Transformational Strategies and Disruptive Innovation

Springer, 2024. — 329 p. — (Advanced Sciences and Technologies for Security Applications). — ISBN: 978-3-031-52271-0.This book discusses a range of topics that are essential to understanding cyber security, including legal implications and technical aspects, cyber detection, and minimizing the threats so that governments and organizations can function without noticeable degradation of service. Unlike other technological threats, cyber security threats have the potential to destroy governments and undermine democratic processes – which makes an overarching cyber security strategy essential for all functioning governments. Thus, the book serves as a guide for developing strategies and ideas in the field and as a motivator for other governments and interested parties to develop and implement effective strategies.Arguably the most difficult aspect of these strategies is their implementation, which will require a cultural sea change in governments’ approaches to handling cyber security and developing a regulatory framework that links organizations and governments in a secure working environment. The development of cyber security strategies calls for new skills at the technical and user levels alike. However, IT skills are sometimes in short supply, and without a government policy on cyber security training, the lack of these skills could hamper the full potential of cyber security. The book explores various aspects and challenges of cyber security strategy and highlights the benefits and drawbacks, offering in-depth insights into the field.Artificial Intelligence (AI), Machine Learning, and Deep Learning are powerful and intelligent technologies that have prevalent applications in the finance domain. These technologies enable financial institutions to develop advanced systems such as fraud detection, portfolio management, market segmentation, stock price prediction, and security anomaly detection. Recent decades have shown a great deal of research applications of AI in various areas of finance. This paper presents the state of ML and DL technologies, their implementation areas in finance, future trends, and challenges.In the context of Machine Learning, algorithms serve as the core mechanisms through which models are constructed, refined, and applied to datasets. These algorithms encompass a spectrum of methodologies that include various learning paradigms, optimization techniques, and mathematical frameworks to facilitate pattern recognition, knowledge extraction, and predictive modeling from data. The efficacy of machine learning algorithms lies in their ability to generalize from training data to make accurate predictions on new, unseen data, thereby contributing to the advancement of data-driven decision-making and automation in diverse fields.The landscape of Machine Learning algorithms is a complex tapestry woven with methodologies meticulously designed to address specific data scenarios and learning objectives. These algorithms can be broadly categorized into three primary classifications: supervised learning, unsupervised learning, and reinforcement learning. Each classification encompasses distinctive principles, techniques, and practical applications, collectively contributing to the multi-faceted realm of machine learning’s capabilities.Impact of Artificial Intelligence on Enterprise Information Security Management in the Context of ISO 27001 and 27002: A Tertiary Systematic Review and Comparative Analysis.
Artificial Intelligence in Healthcare and Medical Records Security.
Implementation of Machine Learning and Deep Learning in Finance.
An Approach to Measure the Effectiveness of the MITRE ATLAS Framework in Safeguarding Machine Learning Systems Against Data Poisoning Attack.
Emerging Trends in Cloud Computing Paradigm: An Extensive Literature Review on Cloud Security, Service Models, and Practical Suggestions.
Technological Governance (Cybersecurity and AI): Role of Digital Governance.
Using Artificial Intelligence (AI) and Blockchain to Secure Smart Cities’ Services and Applications.
Ensuring Securing PII Data in the AWS Cloud: A Comprehensive Guide to PCI DSS Compliance.
Government Strategies on Cybersecurity and How Artificial Intelligence Can Impact Cybersecurity in Healthcare with Special Reference to the UK.
CCAF, Continuous Cyber Assurance Framework.
Global Legislation Muzzling Freedom of Speech in the Guise of Cyber Security.
Cybersecurity Crafting Intervention Model Based on Behaviors Change Wheel.
Reinforcement Learning Model for Detecting Phishing Websites.