Parametric Cyber Defense: A Sophisticated Machine Learning Architecture for Advanced Intrusion Detection and Threat Classification

In contemporary network environments, effective countermeasures against cybersecurity threats require highly effective Intrusion Detection Systems (IDS). This paper proposes an advanced methodology to enhance IDS by employing parametric analysis on network traffic characteristics. Through careful data selection and preprocessing from a comprehensive dataset, data integrity is maintained, with features such as packet size and transfer duration being leveraged. Machine learning algorithms, including Decision Tree, Extra Tree, and xgBoost, are applied to detect and classify various cyberattack categories. In addition, advanced feature selection and dimensionality reduction techniques are utilized to improve model efficiency. Extensive experimentation demonstrates superior accuracy in identifying and classifying cyber threats while reducing the occurrence of false positives. The proposed framework strengthens cybersecurity infrastructures through the integration of advanced machine learning techniques to address evolving threats.