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Cited by 106Network Security and Intrusion DetectionInternet of Things and AIInternet Traffic Analysis and Secure E-voting

The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.

Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

Open access
Kim, Jiyeon, Shim, Minsun, Hong, Seungah, Shin, Yulim, Choi, Eunjung·2020·Applied Sciences
DOI 10.3390/app10197009
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Abstract

As the number of Internet of Things (IoT) devices connected to the network rapidly increases, network attacks such as flooding and Denial of Service (DoS) are also increasing. These attacks cause network disruption and denial of service to IoT devices. However, a large number of heterogenous devices deployed in the IoT environment make it difficult to detect IoT attacks using traditional rule-based security solutions. It is challenging to develop optimal security models for each type of the device. Machine learning (ML) is an alternative technique that allows one to develop optimal security models based on empirical data from each device. We employ the ML technique for IoT attack detection. We focus on botnet attacks targeting various IoT devices and develop ML-based models for each type of device. We use the N-BaIoT dataset generated by injecting botnet attacks (Bashlite and Mirai) into various types of IoT devices, including a Doorbell, Baby Monitor, Security Camera, and Webcam. We develop a botnet detection model for each device using numerous ML models, including deep learning (DL) models. We then analyze the effective models with a high detection F1-score by carrying out multiclass classification, as well as binary classification, for each model.

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Research works printed but not linked: 9

  1. (2020, October 08). Available online: https://www.gartner.com/en/newsroom/press-releases/2019-08-29-gartner-says-5-8-billion-enterprise-and-automotive-io.
  2. Sonar, 2014, A survey: DDOS attack on internet of things, Int. J. Eng. Res. Dev., 10, 58
  3. Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N.O., Guarnizo, J.D., and Elovici, Y. (2017). Detection of unauthorized IoT devices using machine learning techniques. arXiv.
  4. Angrishi, K. (2017). Turning internet of things (iot) into internet of vulnerabilities (iov): Iot botnets. arXiv.
  5. Campus, 2018, Denial-of-service or flooding attack in IoT routing, Int. J. Pure Appl. Math., 118, 29
  6. Dash, 2018, Ransomware auto-detection in IoT devices using machine learning, Int. J. Eng. Sci., 8, 19538
  7. Al-Garadi, M.A., Mohamed, A., Al-Ali, A., Du, X., Ali, I., and Guizani, M. (2018). A Survey of machine and deep learning methods for internet of things (IoT) security. arXiv.
  8. Xie, X., Wu, D., Liu, S., and Li, R. (2017). IoT data analytics using deep learning. arXiv.
  9. Manos, A., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J., Durumeric, Z., Halderman, J.A., Invernizzi, L., and Kallitsis, M. (2017, January 16–18). Understanding the mirai botnet. Proceedings of the 26th {USENIX} Security Symposium ({USENIX} Security 17), Vancouver, BC, Canada.

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Metadata

Authors
Kim, J., Shim, M., Hong, S., Shin, Y., & Choi, E.
Affiliations
Seoul Women's University (KR)
Corresponding author
Kim, Jiyeon
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2020-10-08
Volume
10
Issue
19
Pages
7009
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app10197009
OpenAlex
W3092149785
Cited by
106
References
37
Citations by year
2026: 11 · 2025: 23 · 2024: 26 · 2023: 29 · 2022: 10 · 2021: 7
FWCI
5.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Research Foundation of Korea (NRF-2018R1D1A1B07050543)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Keywords
IoT botnets, botnet detection, Internet of Things security, Mirai, N-BaIoT dataset, multi-class classification, binary classification, denial-of-service attacks, smart home devices

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