Search model for searching the evidence in digital forensic analysis

Digital forensics investigation involves the examination of the digital devices to gather evidence associated with the crime. The amount of data that needs to be processed in a typical forensic investigation today is immense and extremely diverse. Thus finding relevant evidence related to the case is difficult and time-consuming. This research work aims in designing a search model that hunts pertinent evidence by analysing large amount of data for faster and more effective processing in criminal investigation. The proposed work allows the investigator to semantically cluster the data collected from digital devices based on the keyword specified by the investigator or suggested by system. Experiments were conducted on dummy crime dataset to test the accuracy and the scalability of the proposed system. Experimental results proved that subject suggestion improved the accuracy and thus speeds up the process of searching the evidence.