An AI-Driven Cybersecurity Threat Detection and Incident Response Platform for Real-time Network Protection

Kamaluri Mubarak, V. Vijayalakshmi, Dr. S. Usharani

Cybersecurity; Machine Learning; Intrusion Detection; Random Forest; Django; Threat Detection; Network Security.

The rapid expansion of digital infrastructure across industries has significantly increased the vulnerability of systems to cyber threats such as Denial-of-Service attacks, unauthorized access, probing activities, and privilege escalation attempts. Traditional security mechanisms, which rely on signature-based detection, are increasingly ineffective against evolving and unknown attack patterns. This paper presents a Smart Cybersecurity Threat Detection Platform that leverages machine learning techniques for real-time identification and classification of network threats. The proposed system is developed using the Django web framework and Python, integrating a Random Forest classifier trained on a dataset modelled after the NSL-KDD benchmark. The platform processes network traffic parameters such as protocol type, connection duration, source and destination byte counts, connection flags, and connection frequency to classify activities into Normal, DoS, Probe, R2L, and U2R categories. In addition to detection, the system incorporates an Automated Threat Isolation System (ATIS) that identifies high-confidence threats and initiates isolation workflows. A real-time dashboard provides visualization of threat distribution, system performance metrics, and alert management. The system also maintains an audit trail of all activities and supports alert tracking and resolution workflows. The Smart Cybersecurity Threat Detection Platform offers a scalable and intelligent approach to network security by combining anomaly detection, real-time monitoring, and automated response mechanisms. It demonstrates an effective transition from reactive to proactive cybersecurity defense strategies.

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