A hybrid deep learning technique based integrated multi-model data fusion for forensic investigation

Abstract

Digital evidence is an integral part of any trial. Data is critical facts, encrypted information that requires explanation in order to gain meaning and knowledge. The current process of digital forensic research cannot effectively address the various aspects of a complex infrastructure. Therefore, digital forensics requires the optimal processing of a complex infrastructure that differs from the current process and structure. For a long time, digital forensic research has been utilized to discuss these issues. In this research, we offer a forensic investigation hybrid deep learning approach based on integrated multi-model data fusion (HDL-DFI). First, we concentrate on digital evidence collection and management systems, which can be achieved by an integrated data fusion model with the help of an improved brain storm optimization (IBSO) algorithm. Here, we consider several multimedia data’s for evidence purposes, i.e. text, image, speech, physiological signals, and video. Then, we introduce a recurrent multiplicative neuron with a deep neural network (RM-DNN) for data de-duplication in evidence collection, which avoids repeated and redundant data. After that, we design a multistage dynamic neural network (MDNN) for sentimental analysis to decide what type of crime has transpired and classify the action on it. Finally, the accuracy, precision, recall, F1-score, G-mean, and area under the curve of our proposed HDL-DFI model implemented with the standard benchmark database and its fallouts are compared to current state-of-the-art replicas (AUC). The results of our experiments show that the computation time of the proposed model HDL-DFI is 20% and 25% lower than the previous model’s for uploading familiar and unfamiliar files, 22% and 29% lower for authentication generation, 23% and 31% lower for the index service test scenario, and 24.097% and 32.02% lower for familiarity checking.