Enhanced IoT Network Security for Network intrusion detection Model based on Machine Learning Technique

Abstract

Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. The security of IoT infrastructure against cyberattacks is a significant challenge today. This paper presents a machine-learning solution for IoT security, which detects and classifies diverse attacks. Data preprocessing techniques were implemented on the NSL-KDD dataset, which involved feature engineering utilizing the chi-squared method to identify the most significant attributes. Our proposed approach is centered on utilizing stacked Long Short-Term Memory (LSTM) networks, which are adept at identifying temporal dependencies and intricate patterns within the chosen features. Our solution effectively classifies attacks by utilizing LSTM's sequential learning and hierarchical representations to ensure the integrity and reliability of the IoT networks. The research study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges.