Cyber Threat Detection: A Next-Gen AI System for Proactive Threat Detection and User Behavior Analysis
Abishek Nandan · 2025
This paper presents a comprehensive cyber threat detection system that integrates multiple advanced technologies including machine learning, real-time network monitoring, and dark web intelligence. The system employs a multi-layered approach to threat detection, utilizing TensorFlow and scikit-learn for machine learning models, with XGBoost as the primary classifier for threat detection, Django for web interface, and various specialized modules for different aspects of threat detection. The system architecture consists of three main components: an AIpowered threat analysis module using XGBoost for classification, a real-time network monitoring system, and a dark web intelligence gathering module. The AI module utilizes XGBoost for high-accuracy threat classification, complemented by TensorFlow for anomaly detection and pattern recognition, while the network monitoring system employs Scapy for real-time packet analysis and threat detection. The dark web monitoring component continuously scans and analyzes potential threats from underground sources. The system demonstrates high accuracy in threat detection with minimal false positives, providing real-time alerts and automated response mechanisms. This research contributes to the field of cybersecurity by presenting a practical, scalable solution that combines multiple detection methodologies into a unified platform.