Cross Industry Applications Of Cyber Security Frameworks
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Cross-Industry Applications of Cyber Security Frameworks
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 Algorithms Using Scikit and TensorFlow Environments
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.
Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
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.