Machine Learning–Driven Classification of Text-Based Cybercrime under the Indian IT ACT

Cybercrimes encompass crime against children, data breaches, and privacy violations. The increased frequency of cybercrimes due to the quick development of technology emphasizes the necessity of complex systems to analyze and categorize these offenses. There are many opportunities to analyze cybercrime data using Machine Learning (ML) techniques because of its enormous accumulation. This study proposes a model that has the potential to automatically analyze text-based reported cybercrime complaints based on the features by use of Random Forest (RF) and Gradient Boosting (GB) algorithms. This model includes a Bag of Words (BoW) approach for feature engineering to analyze reported cybercrime and suggest relevant Indian IT Act sections, such as Section 66E for privacy protection, Section 43A for reported data breach, and Section 72A for disclosure of information, using Natural Language Processing (NLP) for feature extraction and classification. This strategy enhanced the law and enforcement process by timely and accurately categorizing crime. By automating cyber law and providing timely legal answers to various reported cybercrimes, especially those concerning privacy and data protection, the model improves the capabilities of cybercrime units and achieves high accuracy and precision in anticipating pertinent legal sections.