ATLE2FC: Design of an Augmented Transfer Learning Model for Explainable IoT Forensics using Ensemble Classification

Existing forensic models are majorly focussed towards improving efficiency of data analysis, but a very few models are proposed for intelligent evidence extraction & explain-ability. Inspired by these limitations, this text proposes design of ATLE2FC, a model for explainable IoT forensics using ensemble classification. Performance of the model was compared with various state-of-the-art methods, and it was observed that the proposed model outperforms existing methods in terms of accuracy of evidence-based event detection, classification precision, and evidence classification delay.