AI-Based Network Intrusion Detection System

Abstract

Abstract: This report discusses the research done on the chosen topic, which is Developing an AI-based Network Intrusion Detection System using ML and DL algorithms. Recently we have seen so much progress in Internet and communication technologies it is not just connecting computer networks and people but it is also connecting devices involving Big Data. It has so many benefits in each field which are crucial in today's world like education, health, digital transactions, traveling, and anything we can think of. With so many benefits it comes with its negative effects like cyber-attacks which can happen to anybody who is connected to the Internet. So, Networks and Security become very desirable areas of research and work. To be saved from the attacks we have to detect the cyber-attack and stop the intruder from causing harm to our system. We use IDS (Intrusion Detection System), so we can identify incoming attacks. A Network Intrusion detection system provides security by constantly monitoring the network traffic for suspicious behavior. For building an Intrusion Detection System we need a good dataset with a huge amount of data which is of good quality and can be used for training the System so it can predict the output more accurately. In this paper we used NSL- KDD [6] data set a refined version of the KDD’99 dataset. We developed many models using machine learning and deep learning and compared them for detecting intrusion in networks. We used supervised machine learning and deep learning techniques to train and build many classification models that can differentiate between attacking traffic and normal traffic. We compared the accuracy of every model of different datasets so we can find the model that is performing best for network intrusion detection. After performing all the research and comparison we found that a fully connected Deep Learning model is giving better performance than a machine learning model. We used Autoencoder for feature selection for the best-performing Deep Learning Classification Model.