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September 10, 2025Open Access

Adaptive Multi-Model Cybercrime Identification, Prediction using Machine Learning, and Explainable AI

Key Points

The framework achieves high accuracy in detecting malicious communication, enhancing trust in predictions.
Explainable AI techniques such as LIME and SHAP provide justifications for each prediction made by the system.
The multi-stage pipeline processes cybercrime-related text to improve both classification and interpretability.
Real-time monitoring and continuous learning allow the system to adapt to evolving cybercrime patterns.

Abstract

This paper presents an adaptive multi-model framework for cybercrime identification and prediction by integrating machine learning with explainable artificial intelligence (XAI). A multi-stage pipeline is developed that preprocesses cybercrime-related text, applies advanced ML models for classification, and incorporates XAI techniques such as LIME and SHAP to enhance interpretability. The framework not only achieves high accuracy in detecting malicious communication but also provides human-understandable justifications for each prediction, thereby improving trust and accountability. With realtime monitoring and continuous learning capabilities, the system is designed to evolve with emerging cybercrime patterns, ensuring robustness and applicability in diverse domains such as social media moderation, enterprise communication security, and law enforcement support.

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