Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

Nazia Butt, Ana Shahid, Kashif Naseer Qureshi, Sajjad Haider, Ashraf Osman Ibrahim (), Faisal Binzagr and Noman Arshad
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Nazia Butt: Department of Computer Science, Faculty of Engineering and Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Ana Shahid: Department of Computer Science, Faculty of Engineering and Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Kashif Naseer Qureshi: Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
Sajjad Haider: Department of Computer Science, Faculty of Engineering and Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
Ashraf Osman Ibrahim: Faculty of Computing and Informatics, University Malaysia Sabah, Kota Kinabalu 88400, Malaysia
Faisal Binzagr: Department of Computer Science, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia
Noman Arshad: Department of Computer Science, Bahria University, Islamabad 44000, Pakistan

Mathematics, 2022, vol. 10, issue 23, 1-19

Abstract: The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.

Keywords: internet of things; smart homes; machine learning; intrusion; attacks; detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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