Network Intrusion Detection Using Machine Learning & Deep Learning

Abstract

In recent decades, rapid development in the world of technology and networks has been achieved, also there is a spread of Internet services in all fields over the world. Piracy numbers have increased, also a lot of modern systems were penetrated, so the developing information security technologies to detect the new attack become an important requirement. One of the most important information security technologies is an Intrusion Detection System (IDS) that uses machine learning and deep learning techniques to detect anomalies in the network. The main idea of this paper is to use an advanced intrusion detection system with high network performance to detect the unknown attack package. We use different kind of machine learning algorithm with high accuracy to detect which attack is the most in these dataset. In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 100 number of epochs and KDDCup-‘99‘ dataset has been used for training and benchmarking the network. We compare between both of them on the same dataset .