Network Intrusion Detection System
The Machine Learning based Network Intrusion Detection System detects anomalies through ML algorithms by analysing behaviours of packets. An Intrusion Detection System is primarily used for protection of network and information system. It monitors the operation of a network. Intrusion Detection System monitors network of computers for attacks that are aimed at stealing information. Applying Machine Learning can result in low False Alarm Rate and high detection rate. Machine Learning based Network Intrusion Detection System learns the characteristics of attack traffic based on training data. This approach can provide more robust and more accurate classification with the same classification dataset compared to existing approaches, it will be used as one of the feasible solutions to overcome weakness and limitation of existing Machine Learning based Network Intrusion Detection System. Decision Tree is used for classification and to predict data as normal or intrusive. Model performance was analysed on features extracted from the KDD dataset. In the case of Network Intrusion Detection System models, the network traffic instance will be predicted to belong to either benign (normal) or attack class.