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Combining Genetic Algorithms and Neural Networks for File Forgery Detection

  • Konstantinos ΚarampidisEmail author
  • Ioannis Deligiannis
  • Giorgos Papadourakis
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 149)

Abstract

Today’s electronic devices are so ubiquitous that the collection and use of digital evidence has become a standard part of many criminal and civil investigations. The uncovering and examination of those shreds of evidence is a relatively new and important process to provide crucial information in a court of law. Suspects routinely have their laptops and cell phones examined for corroborating evidence. However, digital forensic investigators are facing several challenges such as file obfuscation, encryption, alteration and a massive amount of evidence. These challenges often lead to incomplete analysis and inadequate conclusions. Consequently, a digital forensic examiner uses specialized forensic software to accurately identify the file types to determine which of them may contain potential evidence.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Konstantinos Κarampidis
    • 1
    Email author
  • Ioannis Deligiannis
    • 2
  • Giorgos Papadourakis
    • 3
  1. 1.Department of Information & Communication Systems EngineeringUniversity of the AegeanKarlovasi, SamosGreece
  2. 2.Department of Cultural Heritage Management and New TechnologiesUniversity of PatrasAgrinioGreece
  3. 3.Department of Informatics EngineeringTechnological Educational Institute of CreteHeraklion, CreteGreece

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