IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i23p4598-d993228.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nazia Butt & Ana Shahid & Kashif Naseer Qureshi & Sajjad Haider & Ashraf Osman Ibrahim & Faisal Binzagr & Noman Arshad, 2022. "Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks," Mathematics, MDPI, vol. 10(23), pages 1-19, December.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4598-:d:993228
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/23/4598/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/23/4598/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4598-:d:993228. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.