An AI-Driven Framework For Data Recovery using Flash – aware Intelligent Block Analysis and Fragment Sequencing For Android File Systems

Abstract

Android devices have become primary sources of digital evidence in modern investigations; nevertheless, reliable data recovery from these devices is still difficult due to flash-based storage behaviour and sophisticated file systems such as EXT4 and F2FS. Traditional rule-based and signature-driven recovery strategies are much less effective due to characteristics such as out-of-place updates, garbage collection, and metadata volatility. This paper proposes an AI-driven data recovery framework for Android file systems using flash-aware intelligent block analysis and fragment sequencing. The framework identifies and prioritizes candidate storage blocks based on flash memory characteristics, applies machine learning models to classify block recoverability, and reconstructs deleted files through sequence-based fragment ordering under temporal and structural constraints. A dedicated validation layer performs structural verification, assigns recovery confidence scores, and provides explainable and repeatable recovery outcomes suitable for forensic use. Experimental evaluation on EXT4 and F2FS storage images demonstrates improved recovery accuracy, reduced false positives, and enhanced robustness under severe fragmentation compared to conventional approaches. The results show that integrating flash-awareness with AI-based intelligence significantly advances the reliability and forensic readiness of Android data recovery[1].