Systematic review for attack tactics, privacy, and safety models in big data systems

Abstract

This systematic review explores cyberattack tactics, privacy concerns, and safety measures within big data systems, focusing on the critical challenges of maintaining data security in today's complex digital environments. The review begins by categorizing various cyberattacks, laying the groundwork for understanding the threats to big data. It identifies key vulnerabilities that compromise privacy and safety, and examines the ethical implications of these issues. The role of artificial intelligence in enhancing security defenses is highlighted as a crucial aspect of mitigating these threats. Additionally, a comparative assessment of regulatory frameworks such as GDPR, NIST, and ISO 27001 is provided, emphasizing the importance of legal and compliance considerations in data protection. The review concludes by proposing a structured approach to cyberattack detection and processing, advocating for strategies that address both technical vulnerabilities and regulatory requirements, followed by a critical discussion on the usability of previous methods for mobile security, highlighting adaptability and performance, discussing explainability and Gen AI adoption. This work offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on cybersecurity in the big data era.