Intrusion_Detection_using_decision_tree
Machine learning has become an essential layer in all the latest intrusion detection systems. It calls for the need of integration of deep neural networks. A machine learning model is used to predict attack detection on the intrusion detection system (IDS). This is done by integrating a machine learning system with deep neural networks (DNNs). In this paper, DNNs have been utilized to predict attacks on IDS using the KDDCup-’99’ dataset for training and benchmarking the network. The performance of machine learning strategies are compared and it is concluded that a DNN of 3 layers performs better than all other classical machine learning algorithms.
Deep neural networks (DNNs) are a very promising tool when it comes to intrusion detection systems (IDS). They are capable of detecting malicious activity in real-time and provide better results than traditional methods.