Feature engineering and deep learning-based intrusion detection framework for securing edge IoT

Muneeba Nasir, Abdul Rehman Javed, Muhammad Adnan Tariq, Muhammad Asim, Thar Baker

Research output: Contribution to journalArticlepeer-review


Devices belonging to the realm of edge Internet of Things (IoT) are becoming highly susceptible to intrusion attacks. The large-scale development in edge IoT, ease of availability, and affordability have drastically increased its usage in the real world. The business market revolves around producing better, innovative, and appealing products every day. However, security is often left unchecked to achieve these standards. Therefore, vulnerabilities present in these devices make them susceptible to various intrusion attacks. We devised a model named DF-IDS for detecting intrusions in IoT traffic. DF-IDS consists of two main phases: In the 1 st phase, it comparatively selects the best features from the feature matrix using SpiderMonkey (SM), principle component analysis (PCA), information gain (IG), and correlation attribute evaluation (CAE). In the 2 nd phase, these features along with assigned labels are used to train a deep neural network for intrusion detection. DF-IDS achieves an accuracy of 99.23% with an F1-score of 99.27%. It shows improvement not only in accuracy but also in F1 score as compared to the other comparative models and existing studies.

Original languageEnglish
Pages (from-to)8852-8866
Number of pages15
JournalJournal of Supercomputing
Issue number6
Publication statusPublished - 13 Jan 2022

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Deep learning
  • Edge IoT
  • Intrusion detection system
  • Machine learning
  • Network security


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