Название: Cybersecurity and Artificial Intelligence: Transformational Strategies and Disruptive InnovationАвтор: Hamid Jahankhani, Gordon Bowen, Mhd Saeed Sharif, Osama Hussien
Издательство: Springer
Год: 2024
Страниц: 329
Язык: английский
Формат: pdf (true), epub
Размер: 40.3 MB
This book discusses a range of topics that are essential to understanding cyber security, including legal implications and technical aspects, cyber detection, and minimising the threats so that governments and organisations 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 organisations 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 modelling 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.
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