OJT-2 AI-Powered Intrusion Detection

Abstract

Intrusion Detection Systems (IDS) are a critical component of modern cyber security infrastructures, safeguarding networks, endpoints, and cloud environments from malicious activities. Traditional IDS solutions rely heavily on predefined rules and signature-based detection, rendering them ineffective against emerging, sophisticated, and zero-day attacks. This paper presents an Artificial Intelligence (AI)-powered intrusion detection system that integrates deep learning and machine learning techniques for real-time threat identification, classification, and automated response. The proposed system employs YOLOv5 for object detection, the face_recognition library for identity verification, and a Random Forest classifier for enhanced decision-making. Additionally, an automated alerting mechanism is implemented via email, ensuring prompt security notifications. The system is designed for high accuracy, reduced false positives, and operational scalability. Experimental results indicate superior detection performance under various lighting and environmental conditions, achieving real-time response speeds while maintaining reliable classification accuracy. This integration of AI-driven detection with automated alerts represents a significant advancement in intrusion prevention, offering robust, adaptive, and scalable protection against evolving cyber threats.