Section Article

A Support Vector Machine with a Polynomial Kernel Trained on Data from Intrusion Detection Systems
Author(s): Dr. Aadesh Sinha

Abstract
Intrusion Detection Systems (IDS) represent one of the most critical security components in modern computing environments where cyberattacks anomalies and unauthorized activities must be identified with high precision. As network traffic grows exponentially and attackers continuously evolve their strategies machine learning has become an essential pillar in designing efficient IDS frameworks. This research paper investigates the fundamental principles implementation dynamics and performance implications of using a Support Vector Machine (SVM) with a polynomial kernel trained on intrusion detection datasets. SVMs are known for their mathematical robustness ability to handle high-dimensional spaces and strong generalization capabilities making them an attractive choice for classification tasks in cybersecurity. The polynomial kernel expands input features into higher-order dimensions enabling the model to capture nonlinear relationships inherent in intrusion patterns.