A Proposed Ensemble Model of Network Intrusion Detection System for binary and Multiclassification

Amrita Bhatnagar¹, Arun Giri¹, Aditi Sharma^2,*

¹Shobhit Institute of Eng. & technology, Meerut, India

²Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, India

Emails: amritapsaxena@gmail.com; arungiri@shobhitunivesity.ac.in; aditi.sharma@ieee.org

Abstract

A network Intrusion detection system is a system that can find out different types of attacks. ANIDS is used to find out the noble type of attack by using machine learning and deep learning techniques. These techniques are very useful to find out those attacks whose patterns are not stored in the database. Therefore, these types of systems need more research to improve their accuracy and reduce the false alarm rate. In this paper, we are going to propose an ensemble framework for NIDS using different ML and DL techniques. In this paper, we have used the XGBOOST algorithm for feature extraction and for classification, CNN and RNN deep learning techniques are used. This ensemble model is used for the binary and multiclassification of attacks. Our model was checked on the dataset CICIDS-2018 which gives a better accuracy and low false alarm rate.

Keywords: Network Intrusion detection system; Denial of service attack; CNN; RNN; XGOBoost