File Fragment Classification Using Grayscale Image Conversion and Deep Learning in Digital Forensics
| Autor: | Rong Li, Qing Liao, Qian Chen, Lucas Chi Kwong Hui, Zoe Lin Jiang, Junbin Fang, Zhengzhong Yi, Xuan Wang, Dong Liu, Siu-Ming Yiu, En Zhang, Guikai Xi |
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| Rok vydání: | 2018 |
| Předmět: |
021110 strategic
defence & security studies business.industry Computer science Deep learning Digital forensics Feature extraction 0211 other engineering and technologies 020207 software engineering Pattern recognition 02 engineering and technology Convolutional neural network Grayscale Image conversion 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Artificial intelligence business Hamming code |
| Zdroj: | IEEE Symposium on Security and Privacy Workshops |
| DOI: | 10.1109/spw.2018.00029 |
| Popis: | File fragment classification is an important step in digital forensics. The most popular method is based on traditional machine learning by extracting features like N-gram, Shannon entropy or Hamming weights. However, these features are far from enough to classify file fragments. In this paper, we propose a novel scheme based on fragment-to-grayscale image conversion and deep learning to extract more hidden features and therefore improve the accuracy of classification. Benefit from the multi-layered feature maps, our deep convolution neural network (CNN) model can extract nearly ten thousands of features through the non-linear connections between neurons. Our proposed CNN model was trained and tested on the public dataset GovDocs. The experiments results show that we can achieve 70.9% accuracy in classification, which is higher than those of existing works. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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