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

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 real-time 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.

Introduction

The rapid rise of digital communication (e.g., social media, messaging platforms) has improved global interaction but also led to a surge in cybercrimes like phishing, fraud, and online harassment. Traditional detection systems, relying on static rules or keyword filters, are insufficient against constantly evolving threats.

???? Proposed Solution

The paper proposes an Adaptive Multi-Model Cyber Crime Identification and Prediction System that combines:

• Machine Learning (ML): To detect suspicious communication patterns.

• Explainable AI (XAI) techniques (LIME, SHAP): To provide transparent explanations for flagged content.

• Real-time monitoring: For instant alerts and adaptive learning.

???? Problem Identification

• Current detection systems lack adaptability and transparency.

• ML models can help but are often "black boxes."

• There's a critical need for real-time, explainable, and adaptive cybercrime detection.

???? Objectives

1. Develop a multi-model ML-based cybercrime detection system.

2. Integrate XAI (LIME, SHAP) for interpretability.

3. Enable real-time monitoring and alerting.

4. Ensure adaptability to new cybercrime patterns.

5. Support applications in social media, corporate security, and law enforcement.

???? Literature Review

• Existing works explore XAI, AI-based cybercrime detection, and security in digital twins (DTs).

• Many focus on specific domains (e.g., smart cities, intrusion detection) and lack holistic, multi-model, explainable frameworks.

• Few studies address real-time, text-based, or voice-based cybercrime with clear interpretability.

?? Research Gaps Identified

• Limited practical deployment of XAI-integrated models.

• Lack of unified, adaptive frameworks covering diverse cybercrime contexts.

• Challenges include high false positives, dataset bias, adversarial threats, and poor generalization.

• Real-time systems with XAI integration are rare, especially for text/voice-based crimes.

?? Methodology

1. Literature Review to assess existing methods and gaps.

2. Data Collection & Preprocessing from cybercrime text datasets.

3. Model Training using ML algorithms to detect malicious patterns.

4. XAI Integration (LIME, SHAP) to explain predictions.

5. System Implementation in Python with real-time alerting.

6. Evaluation using metrics like accuracy, precision, recall, and F1-score.

7. Deployment for use in social media, corporate environments, and law enforcement.

Conclusion

The development of an adaptive multi-model cybercrime detection framework demonstrates how combining machine learning with explainable AI can create a balanced system that is both effective and trustworthy. Beyond its technical performance, the paper highlights the importance of interpretability, scalability, and continuous adaptability in addressing the dynamic landscape of cyber threats. By providing actionable insights and fostering transparency, the framework not only strengthens digital security but also lays the groundwork for future advancements in intelligent, human-centric cyber defense systems.

References

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Copyright

Copyright © 2025 Mrs. Sujay S. Futane, Dr. Pragati Patil, Prof. Nilesh Nagrale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.