Building a Resilient Architecture with an Intelligent System Based on Support Vector Machines Algorithm for Cybersecurity

This research focuses on establishing a competent and sustainable cybersecurity structure stimulated by Support Vector Machine (SVM) algorithms based on detection of intrusions. The paper first provides a clear and concise research method that builds on the benchmark dataset known as the KDD Cup 1999 dataset. In particular, with the help of the data collection, preprocessing, and feature selection, the SVM model gives the opportunity to classify different types of the network attack, such as DoS attack or the user-to-root attack. The systematic approach ensures that only the favorable feature is considered in the model making the model to note the difference between normal traffic and attack traffic. From this study, the developed model was accurate and efficient, with the classification accuracy being 98.7% and F1-score of 96.7% respectively which demonstrated the efficiency of the model in real world applications.Besides, the development of the model, the structure also includes components like real-time control and automatic response. This integration enables the system to scrutinize network traffic in real time and take an appropriate action in case of a threat. Through the automated alerts and the mitigation actions that must be taken once the intrusion is detected the architecture not only identifies infringements but also corrects the violations taking place in the network. This proactive approach is rather helpful nowadays, as the threats are already very high and come very frequently on the digital level. Hazard response capability further strengthens the cybersecurity system, thus crucial in reducing vulnerability and system outages.