Nowadays, the increasing number of crimes with different versatility and intensity leads to the threat against the day-to-day livelihood in the society. To reduce the crimes, it is vital to detect the crime or criminal patterns with the assistance of data mining algorithms. By matching the historical crime patterns with the new patterns, the machine learning algorithms help the digital forensic investigator predict the crimes. In the field of Digital forensics, Statistical tools and techniques have been widely used for detecting the traces of the crime event. Statistical correlations play a crucial role in digital forensic investigation. Statistical data investigation involves problem formulation, planning, acquisition, organization, validation, analysis, interpretation, and presentation of data. The digital forensics models have employed several statistical methods to determine and validate the source of the evidence through assessment. The emergence of digital forensic identification and acquisition tools enables the investigator to perform the investigation under forensically sound conditions. According to the National Institute of Standards and Technology (NIST), the forensic identification and acquisition tools are modeled to perform the forensic investigation legally. Digital forensic tools have been widely utilized to mitigate time consumption while searching digital evidence. To increase the efficiency of the digital investigation, different forensic tools have been developed that support the investigators. The goal of the forensic examination and analysis tools is to find the potential evidence from the extracted evidence for the crime event. To find out the conclusions for the digital forensic investigation, there is the availability of the different digital and physical evidence analysis tools with scientific procedures. The examination and analysis tools focus on an in-depth search of the digital evidence concerning the suspected digital crime and the significance of the evidence determination against the crime.
