Extracting useful information from mobile communication data for forensic investigation

We propose a forensic analysis tool called LDRidentifier that determines the influential members of a criminal organization. Usually, Criminal forensic investigators aim at determining the influential members of criminal organizations in order to eliminate them, which would result in hindering and disrupting the operations of these criminal organizations and put them out of business. LDRidentifier constructs a network representing a criminal organization from Mobile Communication Data belonging to the organization. It then constructs a Minimum Spanning Tree (MST) of the network. It determines the leaders of a criminal organization by identifying the important vertices in the network, using the concept of existence dependency. Each vertex v is assigned a score, which is the number of other vertices, whose existence in the MSP is dependent on v. Criminals represented by the top ranked vertices are considered the influential members. We evaluated the quality of LDRidentifier by comparing it with two other systems. Results showed marked improvement.