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Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning
Open accessAbstract
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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“detectors that achieve >99% accuracy on a single dataset routinely lose 10-20 percentage points when transferred cross-dataset”
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Botnet Attacks Detection in IoT Smart Homes: A Machine Learning Approach
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Advancements in AI-Based Botnet Detection Techniques for IoT Networks: A Comprehensive Survey
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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
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-10-08
- Volume
- 10
- Issue
- 19
- Pages
- 7009
- ISSN
- 2076-3417 (ISSN-L)
- 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
- gold
- doi.org
- 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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