Treffer: Securing trustworthy big data: Comprehensive overview of challenges and Enhanced security with machine learning and blockchain.

Big Data (BD) has become pivotal in shaping business strategies, scientific study, and societal decisions in today’s data-driven world. However, the significant volume and complexity of the data raise security concerns. Establishing trust in BD is crucial to mitigating breaches, protecting confidential data, and ensuring accurate predictions and informed decisions. This research focuses on BD’s security challenges, examining threats like denial of service, ransomware, SQL injection, man-in-the-middle, and poisoning attacks. It underscores the necessity for robust defenses. The article outlines traditional security methods augmented by machine learning (ML) and blockchain (BC). It elaborates on various solutions, including input validation, parameterized queries, firewalls, intrusion detection systems, access control, encryption, and ML’s anomaly detection, predictive analysis, and behavioral analysis, alongside BC’s immutable records, decentralization, and smart contracts for validation. This comprehensive approach can strengthen BD ecosystems, as reported in prior studies, by safeguarding data integrity and confidentiality under specific settings against evolving threats. [ABSTRACT FROM AUTHOR]