Machine Learning-Based Networks Surveillance System with cross-evolution

Abstract: The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overheadDDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset has been used to train and validate. We are used for comparison for 2 dataset (CICIDS2017 and KDD Cup 99 ) Then, we have to implement the Deep learning algorithms is Proposed Method Classification Using LSTM algorithm Model predict .Testing dataset for anomaly detection model finally classified attack or normal. Finally, the experimental results shows that the performance metrics such as accuracy, precision, recall and confusion matrix.