Smart Cyber Defence System Using Artificial Intelligence

A Machine-Learning-Based Web Application for Real-Time Network Threat Detection, Classification, and Automated

Mitigation

Guide Name: A.R. Magar

Satyam Santoshprasad Mishra, Neha Devidas Kadam, Shambhavi Somnath Raut, Avishkar Nilkanth Kurhe
Department of Computer Science and Engineering

Shri Shivaji Institute of Engineering and Management Studies, Parbhani, Maharashtra, India sm90638007@gmail.com

Abstract—The increasing complexity and volume of cyber attacks have rendered traditional signature-based intrusion detection systems inadequate for modern network environments. This paper presents the Smart Cyber Defence System (SCDS), a full-stack Django web application that integrates a supervised machine-learning pipeline with a role-based management portal to provide real-time network threat detection, multi-class classification, and automated IP-level mitigation. A Random Forest classifier is trained on a 63-feature network-traffic dataset covering five threat categories: DDoS, Mal-ware, Phishing, Intrusion, and Benign traffic. A rule-based fallback mechanism ensures continuous protection when no trained model artefact is available. High-severity detections trigger automatic IP blocking persisted in a relational database with full administrator-controlled lifecycle management. The system achieves an overall classification accuracy of 93.4 percent with a macro-averaged F1-score of 0.92 on the hold-out test set, and a prediction latency of 42 ms for trained-model inference. These results demonstrate the practical viability of embedding ML-based cyber defence within an accessible, open-source web platform suitable for organisations without dedicated security teams.

Index Terms—cyber defence, intrusion detection, Random Forest, machine learning, network security, DDoS detection, IP blocking, Django, threat classification, anomaly detection, automated mitigation