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Data Fragment Classification of High Entropy Files Using Machine Learning

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Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing (ICCIC 2022)

Part of the book series: Cognitive Science and Technology ((CSAT))

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Abstract

Accurate and effective file type classification of available evidentiary data in the absence of header and crucial file system information is one of the main challenges in digital forensics. Many approaches have been presented to tackle this issue, including byte frequency histograms, the Oscar method, and the keyword-based approach, but they were ineffective for high-entropy files. For fragments with high entropy data, our method is effective and very accurate. In which feature vectors are derived from normalized byte frequencies of a given fragment, using classification algorithms and forecast the kind of fragment that will be learned under supervision.

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Correspondence to M. Sunitha .

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sunitha, M., Srinivas, K., Adilakshmi, T., Baswaraj, D., Perala, A., Bellamkonda, V. (2023). Data Fragment Classification of High Entropy Files Using Machine Learning. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2746-3_62

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