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File Fragment Analysis For Digital Forensics: ML-Based Type Identification and Unsupervised Clustering by jadhavprathamesh312 is a document available to read on EtoBox.

What is File Fragment Analysis For Digital Forensics: ML-Based Type Identification and Unsupervised Clustering about?

The research internship report presents a dual-stream framework for file fragment analysis in digital forensics, utilizing machine learning for type identification and unsupervised clustering. The proposed system achieves an 81.0% macro F1-score across 22 file types by combining supervised learning methods with DBSCAN clustering, effectively classifying raw binary fragments without metadata. An interactive dashboard is developed for model comparison and real-time predictions, showcasing the effectiveness of

Author

jadhavprathamesh312

Language

EN