Security, Privacy, and Forensics Issues in Big Data

Security, Privacy, and Forensics Issues in Big Data
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Book Description
With the proliferation of devices connected to the internet and connected to each other, the volume of data collected, stored, and processed is increasing every day, which brings new challenges in terms of information security. As big data expands with the help of public clouds, traditional security solutions tailored to private computing infrastructures and confined to a well-defined security perimeter, such as firewalls and demilitarized zones (DMZs), are no longer effective. New security functions are required to work over the heterogenous composition of diverse hardware, operating systems, and network domains.
Security, Privacy, and Forensics Issues in Big Data is an essential research book that examines recent advancements in big data and the impact that these advancements have on information security and privacy measures needed for these networks. Highlighting a range of topics including cryptography, data analytics, and threat detection, this is an excellent reference source for students, software developers and engineers, security analysts, IT consultants, academicians, researchers, and professionals.

Content

Chapter 1. Securing the Cloud for Big Data
Chapter 2. Big dаta: Challenges and Solutions
Chapter 3. Human Factors in Cybersecurity: Issues and Challenges in Big Data
Chapter 4. Security and Privacy Challenges in Big Data
Chapter 5. Cloud-Centric Blockchain Public Key Infrastructure for Big Data Applications
Chapter 6. Security Vulnerabilities, Threats, and Attacks in IoT and Big dаta: Challenges and Solutions
Chapter 7. Threat Hunting in Windows Using Big Security Log Data
Chapter 8. Nature-Inspired Techniques for Data Security in Big Data
Chapter 9. Bootstrapping Urban Planning: Addressing Big Data Issues in Smart Cities
Chapter 10. Securing Online Bank’s Big Data Through Block Chain Technology: Cross-Border Transactions Security and Tracking
Chapter 11. Enhance Data Security and Privacy in Cloud
Chapter 12. The Unheard Story of Organizational Motivations Towards User Privacy
Chapter 13. Botnet and Internet of Things (IoTs): A Definition, Taxonomy, Challenges, and Future Directions
Chapter 14. A Conceptual Model for the Organizational Adoption of Information System Security Innovations
Chapter 15. Theoretical Foundations of Deep Resonance Interference Network: Towards Intuitive Learning as a Wave Field Phenomenon
Chapter 16. Malware Threat in Internet of Things and Its Mitigation Analysis
Chapter 17. Leveraging Fog Computing and Deep Learning for Building a Secure Individual Health-Based Decision Support System to Evade Air Pollution

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