Abstract

Nowadays, cybercrime has been soaring, which has necessitated the use of both automatic and efficient methods of identifying and classifying possible cases. This paper, in which we utilize text from the complaints section of electronic magazines, news stories, incident reports, and evaluations of proceedings from regulatory establishments, provides a machine learning-based method for cybercrime classification. Vectorization using Term Frequency-Inverse Document Frequency (TF-IDF) is used to transform the dataset after it is preprocessed with Natural Language Processing (NLP) techniques. Machine Learning (ML) models like Random Forest (RF) , Gradient Boosting (GB), and an Ensemble Classifier, together with the proposed Trinetra framework, are selected for evaluation using standard performance metrics. In this study, the potential of the online version of the cybercrime detection system using automatic techniques was demonstrated by the amount of accuracy, such as the proposed approach Trinetra showed higher accuracy than the others.

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