Use of Machine Learning Algorithm on File Metadata for Digital Forensic Investigation Process

  • Esan P. PanchalEmail author
  • Shruti B. YagnikEmail author
  • B. K. SharmaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)


File metadata consists of the various file properties like file name, file size, date of access and modifications, date of creation, and hash code of files. This metadata can be useful for detecting various activities performed by a user on a specific system through files. In order to analyze any crime, it is necessary to focus upon the metadata instead of the data of the file. Just acquiring the data from files is not enough, it is equally important to analyze its metadata, which may direct a digital forensic investigator toward the suspicious system. Analyzing the metadata will reveal the evidences of the committed crimes which would be useful in the further phases of the investigation process. Our research paper focuses on analyzing file metadata by applying machine learning algorithms that will be useful for digital forensic investigation.


Cyber-crime Cyber-forensics Evidences Forensic investigations Machine learning algorithms Clustering Digital forensic File metadata 


  1. 1.
    Panchal EP (2013) Extraction of persistence and volatile forensics evidences from computer system. Int J Comput Trends Technol (IJCTT) 4(5):964–968. ISSN 2231-2803. Published by Seventh Sense Research Group
  2. 2.
    Patel PC (2013) Aggregation of digital forensics evidences. Int J Comput Trends Technol (IJCTT) 4(4):881–884. ISSN 2231-2803. Published by Seventh Sense Research Group
  3. 3.
    Abbas OA (2008) Comparisons between data clustering algorithms. Int Arab J Inf Technol (IAJIT) 5(3)Google Scholar
  4. 4.
    Yagnik SB (2013) Requirements to build a system that uses machine learning based approach for analysis of forensic data. Int J Comput Trends Technol (IJCTT) 4(4):927–932. ISSN 2231-2803. Published by Seventh Sense Research Group
  5. 5.
    Machine learning (1997). T.M. Mitchell, McGraw HillGoogle Scholar
  6. 6.
    “Clustering-Kmeans”. Accessed on 15 Apr 2017

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentRai UniversityAhmedabadIndia
  2. 2.Computer Science and Engineering DepartmentCalorx Teachers’ UniversityAhmedabadIndia
  3. 3.Bhartiya Gyanpeeth MahavidyalayaUjjainIndia

Personalised recommendations