Deep Learning-Based AI Modeling, Intrusion Detection
Madhab Paul Choudhury, Madhab Paul Choudhury, Chandrashekhar Azad · Advances in digital crime, forensics, and cyber terrorism book series · 2022
Machine learning techniques are being used to create an intrusion detection system (IDS) for detecting and classifying cyber-attacks at the network-level and the host-level in a timely manner. Various datasets are available for research by cyber security researchers. However, no previous study has shown the detailed analysis of the performance of various machine learning algorithms on various available datasets. As the nature of malware is changing dynamically with the changing attacking methods, the detailed analysis of the available data sets is necessary to find out the cause of the malware datasets, and accordingly, necessary steps can be executed for maintaining the security of the network. A deep neural network (DNN) is being explored to develop an effective intrusion detection system. The optimal network parameters and network topologies for DNNs are chosen through the following hyper parameter selection methods with KDD Cup 99 dataset. The DNN model can be applied on KDD Cup 99 and on other datasets also such as NSL-KDD, UNSW-NB15, Kyoto to conduct the experiment.