Abstract:

The rapid growth of computer networks, cloud computing, and Internet of Things (IoT) environments has significantly increased the complexity and frequency of cyber threats. Traditional signature-based intrusion detection systems are effective for known attacks but fail to detect emerging and evolving threats. To address these limitations, this paper proposes an adaptive machine learning framework for network intrusion detection.

The proposed framework integrates data preprocessing, class imbalance handling, multi-model learning, and a feedback-driven adaptive learning mechanism to enable continuous model improvement in dynamic network environments. The system incorporates both machine learning and deep learning models, including Random Forest, Support Vector Machine, Convolutional Neural Networks, and Long Short-Term Memory networks.

Experimental evaluation is conducted on benchmark datasets such as NSL-KDD, CICIDS, and UNSW-NB15 using standard performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC. The results demonstrate that deep learning models achieve superior detection performance, while the adaptive framework enhances robustness and adaptability under changing network conditions.

The study highlights the importance of adaptive learning in modern intrusion detection systems and provides a scalable and effective solution for real-world cybersecurity applications.

Keywords:

Network Intrusion Detection System, Machine Learning, Deep Learning, Cybersecurity, Adaptive Intrusion Detection, Concept Drift Detection

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