Machine Learning Approaches for Classifying Encrypted Files

Encryption is vital for protecting information by converting it into an unreadable format. However, it is also used for anti-forensic purposes, complicating the analysis of files. Clas-sifying encrypted data without decryption is essential for digital forensic analysis, providing insights into encrypted file types. This research explores the use of machine learning models with advanced feature extraction techniques to classify file types in their encrypted form, enhancing digital forensic capabilities and understanding encryption's impact on classification. We applied feature extraction methods to analyze encrypted files and trained machine learning classifiers to predict file types. Our results show significant variance in classification accuracy between different files, demonstrating the feasibility of using machine learning for encrypted file classification while highlighting the importance of feature extraction for classification success.