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Cited by 270

The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.

Design and Development of RNN Anomaly Detection Model for IoT Networks

Open access
Ullah, Imtiaz, Mahmoud, Qusay H.·2022·IEEE Access
DOI 10.1109/access.2022.3176317
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Abstract

Cybersecurity is important today because of the increasing growth of the Internet of Things (IoT), which has resulted in a variety of attacks on computer systems and networks. As the number of various IoT devices and services grows, cyber security will become an increasingly difficult issue to manage. Malicious traffic identification using deep learning techniques has emerged as a key component of network-based intrusion detection systems (IDS). Deep learning methods have been a research focus in network intrusion detection. A recurrent neural network is useful in a wide range of applications. This paper proposes a novel deep learning model for detecting anomalies in IoT networks using recurrent neural networks. The proposed model is implemented in IoT networks utilizing LSTM, BiLSTM, and GRU-based approaches for anomaly detection. A convolutional neural network can analyze input features without losing important information, making them particularly well suited for feature learning. In addition, we propose a hybrid deep learning model based on convolutional and recurrent neural networks. Finally, employing LSTM, BiLSTM, and GRU-based techniques, we propose a lightweight deep learning model for binary classification. The proposed deep learning models are validated using NSLKDD, BoT-IoT, IoT-NI, MQTT, MQTTset, IoT-23, and IoT-DS2 datasets. Our proposed binary and multiclass classification model achieved high accuracy, precision, recall, and F1 score compared to current deep learning implementations.

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Show all 88 references

Research works printed but not linked: 12

  1. 2021, 5 Layer Architecture of Internet of Things
  2. Sarangam, 2021, 7 IoT Layers That You Should Know in 2021
  3. 10.1016/j.asoc.2021.107859 · doi:10.1016/j.asoc.2021.107859
  4. 10.1109/ICCSEC.2017.8447022 · doi:10.1109/iccsec.2017.8447022
  5. 10.1007/s00521-018-3383-7 · doi:10.1007/s00521-018-3383-7
  6. 10.3390/bdcc5040072 · doi:10.3390/bdcc5040072
  7. 10.1109/BigData.2017.8258163 · doi:10.1109/bigdata.2017.8258163
  8. Olah, 2021, Understanding LSTM Networks
  9. Liu, 2017, A Practical Guide to ReLU
  10. Ba, 2016, Layer normalization
  11. Saha, 2018, A Comprehensive Guide to Convolutional Neural Networks
  12. Ullah, 2021, IoT Network Intrusion Datasets

Cited by

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Metadata

Authors
Ullah, I., & Mahmoud, Q. H.
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022
Volume
10
Pages
62722-62750
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2022.3176317
OpenAlex
W4285106051
Cited by
270
References
88
FWCI
24.94
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/legalcode (vor)
Funders
Natural Sciences and Engineering Research Council of Canada

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CoChat. (2026). Citation check: Design and Development of RNN Anomaly Detection Model for IoT Networks. https://app.cochat.ai/papers/10.1109/access.2022.3176317
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Ullah, I., & Mahmoud, Q. H. (2022). Design and Development of RNN Anomaly Detection Model for IoT Networks. IEEE Access. https://doi.org/10.1109/access.2022.3176317

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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