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ZHANG Li, HAO Shengang, ZHANG Quanxin. A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.206
Citation: ZHANG Li, HAO Shengang, ZHANG Quanxin. A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.206

A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery

doi: 10.1049/cje.2020.00.206
Funds:  This work was supported by the National Natural Science Foundation of China (61802210)
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  • Author Bio:

    was born in 1978. She received the Ph.D. degree in computer application technology from Beijing Institute of Technology, in 2013. She is currently an Associate Professor of the Zhejiang University of Media and Communication. Her current research interests include digital forensics, machine learning and information security. (Email: nythhsg@163.com)

    was born in 1977. He received the M.S. degree in computer application technology from Wuhan University of Technology, in 2007. He is now a Ph.D. candidate of Beijing Institute of Technology. His research interests include machine learning and intelligent manufacturing. (Email: jsjxjw@sina.com)

    (corresponding author) was born in 1974. He received the Ph.D. degree in computer application technology from Beijing Institute of Technology, in 2003. He is currently an Associate Professor of Beijing Institute of Technology. His current research interests include deep learning and information security. (Email: zhangqx@bit.edu.cn)

  • Received Date: 2020-07-08
  • Accepted Date: 2020-08-28
  • Available Online: 2021-08-18
  • Data recovery from flash memory in the mobile device can effectively reduce the loss caused by data corruption. Type recognition of data fragment is an essential prerequisite to the low-level data recovery. Previous works in this field classify data fragment based on its file type. Still, the classification efficiency is low, especially when the data fragment is a part of a composite file. We propose a fine-grained approach to classifying data fragment from the low-level flash memory to improve the classification accuracy and efficiency. The proposed method redefines flash-memory-page data recognition problem based on the encoding format of the data segment, and applies a hybrid machine learning algorithm to detect the data type of the flash page. The hybrid algorithm can significantly decompose the given data space and reduce the cost of training. The experimental results show that our method achieves better classification accuracy and higher time performance than the existing methods.
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