An Efficient Intrusion Detection System to Combat Cyber Threats using a Deep Neural Network Model

Mangayarkarasi Ramaiah; C. Vanmathi; Mohammad Zubair Khan; M. Vanitha; M. Deepa

doi:10.5614/itbj.ict.res.appl.2023.17.3.2

Authors

Mangayarkarasi Ramaiah School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India, 632014
C. Vanmathi VIT UniversitySchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India, 632014
Mohammad Zubair Khan Taibah University
M. Vanitha School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India, 632014
M. Deepa School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India, 632014

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2023.17.3.2

Keywords:

artificial deep neural network, correlation tool, DDoS, machine learning, network intrusion detection system, RF-score, XGBoost-score

Abstract

The proliferation of Internet of Things (IoT) solutions has led to a significant increase in cyber-attacks targeting IoT networks. Securing networks and especially wireless IoT networks against these attacks has become a crucial but challenging task for organizations. Therefore, ensuring the security of wireless IoT networks is of the utmost importance in today?s world. Among various solutions for detecting intruders, there is a growing demand for more effective techniques. This paper introduces a network intrusion detection system (NIDS) based on a deep neural network that utilizes network data features selected through the bagging and boosting methods. The presented NIDS implements both binary and multiclass attack detection models and was evaluated using the KDDCUP 99 and CICDDoS datasets. The experimental results demonstrated that the presented NIDS achieved an impressive accuracy rate of 99.4% while using a minimal number of features. This high level of accuracy makes the presented IDS a valuable tool.

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