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Deep Learning for Digital Forensics: Advancing Criminal Investigations
Abstract
Digital forensics has evolved significantly with advancements in artificial intelligence (AI) to enable faster, more accurate, and more scalable criminal investigation evidence collection. Traditional forensic procedures are overwhelmed by the volume, complexity, and fragility of digital evidence. AI-based forensics merges machine learning, predictive analytics, and automation in order to make digital artifact identification, preservation, and analysis better. This essay describes cutting-edge AI-based methods of forensic investigation, including deep learning-based anomaly detection, decision trees-based forensic classification, and ensemble methods to variance reduction in forensic predictions. Using AI, forensic analysts can spot patterns, follow cybercrime patterns, and relate digital evidence with greater precision. AI-based applications also assist in automating routine forensic work, reducing human errors, and improving legal investigations.
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