Hybrid PAACDA Framework With Random Forests for Data Corruption Detection in Complex Systems

Abstract

In the era of big data, maintaining data integrity is crucial, as even minor data corruption cansignificantly distort analytical outcomes in domains such as e-commerce behavior prediction, socialnetwork analysis, and intrusion detection systems. Manual inspection and traditional anomaly detection techniques often fail to identify subtle