Anomaly Detection in Cybersecurity: Leveraging Machine Learning for Intrusion Detection

An intrusion Detection system is a software that monitors the network traffic and alerts the administrators if any malicious request is found. The system is able to segregate between a malicious and non-malicious request using two methods one is signature-based and the other is by training a machine learning model which is also called the Anomaly-based method. In this paper, we have achieved the highest accuracy from all existing models i.e., 99.99%. We have been able to increase the accuracy of ML models as a result of our proposed model, which is an ensemble of Random Forest, Decision tree, and KNN. Along with this, we have also applied a deep learning model ANN which has led to the effect. Since we have used four different datasets, our model is trained with a large number of datasets which has also contributed to high accuracy.