@inproceedings{2e81e522b1b64096b90d862fa82d9693,
title = "File fragment classification using grayscale image conversion and deep learning in digital forensics",
abstract = "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.",
keywords = "Deep learning, Digital forensics, File fragments classification, Grayscale image",
author = "Qian Chen and Qing Liao and Jiang, \{Zoe L.\} and Junbin Fang and Siuming Yiu and Guikai Xi and Rong Li and Zhengzhong Yi and Xuan Wang and Hui, \{Lucas C.K.\} and Dong Liu and En Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018 ; Conference date: 24-05-2018",
year = "2018",
month = aug,
day = "2",
doi = "10.1109/SPW.2018.00029",
language = "英语",
isbn = "9780769563497",
series = "Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "140--147",
booktitle = "Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018",
address = "美国",
}