Intrusion Detection using Machine Learning Techniques

An Intrusion is an uncredited access to a computer in your organization or a personal computer. As the world is becoming more internet-oriented and data leaks occur more than ever in our tech-savvy world, we need to know about these attacks so that they can be prevented hence coming into action Intrusion Detection System. IDS are systems that alert about the attack by analyzing the traffic on the network for signs of unauthorized activity. To identify the attack and alert about that possible attack, this system needs to be trained on some previous attacks data, for this study, the improved version of the KDD99 dataset, NSL-KDD dataset have been used for training the Machine Learning Model. In this analysis of Machine Learning algorithms, the algorithms under consideration are Logistic Regression, Support Vector Machine, Decision Tree, Random Forest. For comparison of the performance of the algorithms metrics like Accuracy Score, Confusion Matrix, and Classification Report were considered to find the best algorithm among them.