"This book posits that rather than cybersecurity being simply an issue of securing systems and networks, corporate leaders need to think in terms of assuring the integrity and durability of the interconnected business and social structures that sit on top of an increasingly complex technology landscape"--

Data is the most important commodity, which is why data protection has become a global priority. Data breaches and security flaws can jeopardize the global economy. Organizations face a greater risk of failing to achieve strategy and business goals as cyber threat behavior grows in frequency, sophistication, and destructiveness. A breach can result in data loss, business interruption, brand and reputation harm, as well as regulatory and legal consequences. A company needs a well-thought-out cybersecurity strategy to secure its critical infrastructure and information systems in order to overcome these challenges. Cross-Industry Applications of Cyber Security Frameworks provides an understanding of the specific, standards-based security controls that make up a best practice cybersecurity program. It is equipped with cross-industry applications of cybersecurity frameworks, best practices for common practices, and suggestions that may be highly relevant or appropriate in every case. Covering topics such as legal frameworks, cybersecurity in FinTech, and open banking, this premier reference source is an essential resource for executives, business leaders, managers, entrepreneurs, IT professionals, government officials, hospital administrators, educational administrators, privacy specialists, researchers, and academicians.

Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.

Melvin Greer and Kevin Jackson have assembled a comprehensive guide to industry-specific cybersecurity threats and provide a detailed risk management framework required to mitigate business risk associated with the adoption of cloud computing. This book can serve multiple purposes, not the least of which is documenting the breadth and severity of the challenges that today’s enterprises face, and the breadth of programmatic elements required to address these challenges. This has become a boardroom issue: Executives must not only exploit the potential of information technologies, but manage their potential risks. Key Features • Provides a cross-industry view of contemporary cloud computing security challenges, solutions, and lessons learned • Offers clear guidance for the development and execution of industry-specific cloud computing business and cybersecurity strategies • Provides insight into the interaction and cross-dependencies between industry business models and industry-specific cloud computing security requirements

The subject of Artificial Intelligence (AI) is continuing on its journey of affecting each and every individual and will keep on this path in the times to come. This handbook is a collection of topics on the application of artificial intelligence applications for sustainability in different areas. It provides an insight into the various uses of concepts and practical examples for different domains all in one place, which makes it unique and important for the potential reader. Handbook of Artificial Intelligence Applications for Industrial Sustainability: Concepts and Practical Examples examines the influence of AI and how it can be used in several industries to improve corporate performance, reduce security concerns, improve customer experience, and ultimately generate value for customers and maximize profits. The handbook offers practical examples, concepts, and applications that provide an easy understanding and implementation process. It provides AI applications in many fields, such as sustainable credit decisions, cyber security and fraud prevention, warehouse management, and much more. This handbook will provide insight to customers, managers, professionals, engineers, researchers, and students on the various uses of AI and sustainability in different domains. All of this needed information compiled into one handbook makes it unique and important for the engineering, business, and computer science communities.

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.

Cybersecurity threats have become ubiquitous and continue to topple every facet of the digital realm as they are a problem for anyone with a gadget or hardware device. However, there are some actions and safeguards that can assist in avoiding these threats and challenges; further study must be done to ensure businesses and users are aware of the current best practices. Cybersecurity Issues, Challenges, and Solutions in the Business World considers cybersecurity innovation alongside the methods and strategies for its joining with the business industry and discusses pertinent application zones such as smart city, e-social insurance, shrewd travel, and more. Covering key topics such as blockchain, data mining, privacy, security issues, and social media, this reference work is ideal for security analysts, forensics experts, business owners, computer scientists, policymakers, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.