Multi-Class Cyber Threat Prediction Using an Intelligent Intrusion Detection System

Digital infrastructure is becoming more complex and then stays to protect them through robust security mechanisms, which have to become more advanced. The idea of this work is to develop an intelligent intrusion detection system to predict and classify the cyberattacks using an ensemble learning techniques with high accuracy. The AdaBoost, Stacking Classifier, and XGBoost algorithms are considered by the system, with the benefit of the use of these algorithms for improved detection rate dealing with the data and controlling the false detection positively. So this dataset is used to train this model which is trained to change attack patterns and also adapt the anomaly at both the time. To obtain excellent performance for multiple settings, extensive evaluation of the proposed framework based on the performance metrics of accuracy precision, recall and F1 score is carried out. The system is also real time and able to quickly identify potential examples of threats for timely conveying of useful insights that support the network resilience. In this work an advanced analysis is done on which advanced learning techniques are applied to increase the development of more efficient ones and thus generate a safe digital environment through a highly scalable solution for protecting critical infrastructures. It offers a good basis for further research, namely, that to face this dynamic and increasing and growing challenge of dynamic cyber threats, the cybersecurity of the future will be better.