Identification and Restoration of LZ77 Compressed Data Using a Machine Learning Approach

Beom Kwon, Myongsik Gong, Jungwoo Huh, Sanghoon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Identifying the type of a codec that used to compress data is essential in digital forensics since many trials and errors required to restore data can be reduced. Nevertheless, most compression algorithms have been configured by using several parameters whose values can be different according to each user. Therefore, in order to restore data more effectively, the values of parameters as well as the type of the codec must be identified. In this paper, we present an identification and restoration method for Lempel-Ziv-77 (LZ77) compressed data. In the proposed method, we identify whether a given data is compressed by LZ77 or not. Moreover, we estimate the values of parameters that were used for compression. Using the estimated parameters, we restore the original data from the LZ77 compressed data. The simulation results demonstrate the feasibility and effectiveness of the proposed method with a successful compression identification and parameter estimation accuracies of 100% and 84.41%.

Original languageEnglish
Title of host publication2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1787-1790
Number of pages4
ISBN (Electronic)9789881476852
DOIs
Publication statusPublished - 2019 Mar 4
Event10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
Duration: 2018 Nov 122018 Nov 15

Publication series

Name2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
CountryUnited States
CityHonolulu
Period18/11/1218/11/15

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Kwon, B., Gong, M., Huh, J., & Lee, S. (2019). Identification and Restoration of LZ77 Compressed Data Using a Machine Learning Approach. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings (pp. 1787-1790). [8659755] (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/APSIPA.2018.8659755