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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Κarampidis, K., Deligiannis, I., Papadourakis, G. (2019). Combining Genetic Algorithms and Neural Networks for File Forgery Detection. In: Tsihrintzis, G., Sotiropoulos, D., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 149 . Springer, Cham. https://doi.org/10.1007/978-3-319-94030-4_12
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