Intelligent Approach to Cyber Security provides details on the important cyber security threats and its mitigation and the influence of Machine Learning, Deep Learning and Blockchain technologies in the realm of cyber security. Features: Role of Deep Learning and Machine Learning in the Field of Cyber Security Using ML to defend against cyber-attacks Using DL to defend against cyber-attacks Using blockchain to defend against cyber-attacks This reference text will be useful for students and researchers interested and working in future cyber security issues in the light of emerging technology in the cyber world.

Cyber Security Threats and Challenges Facing Human Life provides a comprehensive view of the issues, threats, and challenges that are faced in the cyber security domain. This book offers detailed analysis of effective countermeasures and mitigations. The financial sector, healthcare, digital manufacturing, and social media are some of the important areas in which cyber-attacks are frequent and cause great harm. Hence, special emphasis is given to the study and analysis of cyber security challenges and countermeasures in those four important areas. KEY FEATURES * Discusses the prominence of cyber security in human life * Discusses the significance of cyber security in the post-COVID-19 world * Emphasizes the issues, challenges, and applications of cyber security mitigation methods in business and different sectors * Provides comphrension of the impact of cyber security threats and challenges in digital manufacturing and the internet of things environment * Offers understanding of the impact of big data breaches and future trends in data security This book is primarily aimed at undergraduate students, graduate students, researchers, academicians, and professionals who are interested in exploring their research and knowledge in cyber security domain.

1. Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms. 2. Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics. 3. Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems. 4. Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data.