In the cyber forensics industry, it is common to use a combination of tools and techniques to collect evidence for a report. The main tools are the investigative software, a virtual assistant or an automated report generation tool. These software can be used by any company providing forensic services, whether they are legal, security or IT security specialists.
One of the automated analysis techniques is deep learning, which is a type of machine learning. It is based on artificial neural networks, composed of many layers that are made up of nodes with connections between them.
The following are examples of traditional forensic analysis methods: Serology, chromatography, spectroscopy, hair and fibre analysis (such as DNA examination) Pathology, anthropology, odontology, toxicology, structural engineering, and document examination are some of the fields that are investigated.
Forensic analysis is a process of analysing a crime by locating evidence that establishes who, what, when, where, and why the crime occurred. Deoxyribonucleic acid, or DNA, computer, handwriting, bloodstain, and statement analysis are five common methods of forensic analysis.
Database forensics, disc and data capture, email analysis, file analysis, file viewers, internet analysis, mobile device analysis, network forensics, and registry analysis are just a few examples of digital forensics technologies.