Cybercrime Threat Detection and Classification Using Data Analytics Techniques

Abstract

The rapid growth of digital technologies and online communication has significantly increased cybercrime activities, creating major challenges for cybersecurity professionals. Traditional security systems are often unable to detect sophisticated and unknown attacks efficiently. This paper proposes CyberDetect, a machine learning-based cybercrime pattern detection framework using data analytics techniques. The proposed system integrates network traffic analysis, anomaly detection, threat classification, and real-time monitoring to identify malicious activities in large-scale network environments. The framework utilizes machine learning algorithms for detecting suspicious behavioral patterns and classifying cyber threats based on historical datasets. Experimental evaluation was conducted using benchmark cybersecurity datasets including CICIDS2017 and UNSW-NB15. The proposed model achieved an accuracy of 96.4%, precision of 95.8%, recall of 94.9%, and F1-score of 95.3%, outperforming traditional signature-based detection systems. The developed dashboard provides real-time visualization of cyber threats, attack trends, and alert notifications, enabling faster incident response and improved cybersecurity management.