Combining Genetic Algorithms and Neural Networks for File Forgery Detection

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.