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

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Abstract

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.

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Notes

  1. https://www.gsma.com/newsroom/press-release/new-gsma-study-operators-must-look-beyond-connectivity-to-increase-share/.

  2. https://portswigger.net/daily-swig/realtek-sdk-vulnerabilities-impact-dozens-of-downstream-iot-vendors.

  3. https://www.imperva.com/blog/malware-analysis-mirai-ddos-botnet/.

  4. https://securityintelligence.com/posts/internet-of-threats-iot-botnets-network-attacks/.

  5. theguardian.com/technology/2020/aug/26/new-zealand-stock-exchange-hit-by-cyber-attack-for-second-day.

  6. https://towardsdatascience.com/a-deeper-dive-into-the-nsl-kdd-data-set-15c753364657.

  7. https://www.unb.ca/cic/datasets/nsl.html.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan

    Muneeba Nasir, Muhammad Adnan Tariq & Muhammad Asim

  2. Department of Cyber Security, Air University, Islamabad, Pakistan

    Abdul Rehman Javed

  3. Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, UAE

    Thar Baker

Authors
  1. Muneeba Nasir
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  2. Abdul Rehman Javed
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  3. Muhammad Adnan Tariq
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  4. Muhammad Asim
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  5. Thar Baker
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Correspondence to Abdul Rehman Javed.

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Nasir, M., Javed, A.R., Tariq, M.A. et al. Feature engineering and deep learning-based intrusion detection framework for securing edge IoT. J Supercomput 78, 8852–8866 (2022). https://doi.org/10.1007/s11227-021-04250-0

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