An Adaptive and Intelligent Network Defense Framework for Real Time Cyber Threat Detection and Automated Response
TL;DR
Experimental results demonstrate that ensemble-based models, particularly XGBoost and Random Forest, achieved superior performance in terms of accuracy, precision, recall, and F1-score, while maintaining low false positive rates suitable for real-time deployment.
Abstract
The rapid growth of interconnected digital systems and Internet-enabled technologies has significantly increased the frequency and sophistication of cyber threats, thereby exposing modern networks to severe security risks. Traditional signature-based intrusion detection mechanisms are increasingly ineffective against zero-day, polymorphic, and evolving attacks, necessitating the development of intelligent and adaptive security solutions. This study proposes an adaptive and intelligent Network Intrusion Detection and Prevention System (NIDPS) for real-time cyber threat detection and automated response using machine learning techniques. The proposed framework integrates real-time traffic monitoring, feature extraction, supervised machine learning–based classification, and automated intrusion prevention within a unified architecture. Multiple machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost, were implemented and evaluated using benchmark intrusion detection datasets. Comprehensive preprocessing, feature selection, and class imbalance handling techniques were employed to enhance detection accuracy and reduce false alarm rates. Experimental results demonstrate that ensemble-based models, particularly XGBoost and Random Forest, achieved superior performance in terms of accuracy, precision, recall, and F1-score, while maintaining low false positive rates suitable for real-time deployment. The system also exhibited low detection latency and efficient resource utilization, indicating its applicability to enterprise and IoT-enabled network environments. The results confirm that integrating machine learning–driven intrusion detection with automated prevention mechanisms significantly improves network security effectiveness compared to conventional approaches. The proposed framework provides a practical, scalable, and adaptive solution for real-time cyber defense in modern heterogeneous network infrastructures.