A New Intrusion Detection System Based on Convolutional Neural Network

In 2020 only, 36 billion records have been leaked, 95% of those attacks have been caused by human error. Therefore organizations have been looking for multiple technologies to secure there system. One of the modern techniques is using machine learning for traffic classification which help us detect attacks based on monitoring data flow in our network or our workstation. In this paper we presents an implementation of new proposed model based on convolutional neural network (CNN) and long short term memory (LSTM). It is evident from the investigations that different machine learning methods can be used for intrusion detection. Further, the results demonstrated that the usage of machine learning techniques produce positive impact on improving the overall performance of the intrusion detection system in terms of accuracy and lowering false negatives. We using two of most known Deep Learning algorithms CNN and GRU (Convolutional Neural Network, Gated Recurrent Unit) to have a base ground regarding the performance of our proposed model. we are getting encouraging results with implementation of two of famous data-set (NSL-KDD and CIC-IDS2018) We tested our models on binary and multi-class classification for further observation.