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Digital forensics (sometimes known as digital forensic science) is a branch of forensic science encompassing the recovery, investigation, examination, and analysis of material found in digital devices, often in relation to mobile devices and computer crime.
During the 1980s, most digital forensic investigations consisted of "live analysis", examining digital media directly using non-specialist tools. In the 1990s, several freeware and other proprietary tools (both hardware and software) were created to allow investigations to take place without modifying media.
The discipline involves similar techniques and principles to data recovery, but with additional guidelines and practices designed to create a legal audit trail. Evidence from computer forensics investigations is usually subjected to the same guidelines and practices as other digital evidence.
The digital forensic process is a recognized scientific and forensic process used in digital forensics investigations. [1] [2] Forensics researcher Eoghan Casey defines it as a number of steps from the original incident alert through to reporting of findings. [3] The process is predominantly used in computer and mobile forensic investigations ...
Network forensics is a sub-branch of digital forensics relating to the monitoring and analysis of computer network traffic for the purposes of information gathering, legal evidence, or intrusion detection. [1] Unlike other areas of digital forensics, network investigations deal with volatile and dynamic information.
The objective of forensic search software is to allow a person with only a general knowledge of computers, but skilled in document review or investigation techniques, to undertake and search user created electronically stored information (ESI). Data that is typically considered to be user created ESI is made up of emails, documents, pictures ...
Digital forensics is a branch of the forensic sciences related to the investigation of digital devices and media. Within the field a number of "normal" forensics words are re-purposed, and new specialist terms have evolved.
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.