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Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

Computer Science and Technology College, Zhejiang University, Hangzhou 310027, China
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Sensors 2021, 21(4), 1113; https://doi.org/10.3390/s21041113
Received: 27 November 2020 / Revised: 7 January 2021 / Accepted: 20 January 2021 / Published: 5 February 2021
(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)
IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers. View Full-Text
Keywords: IoT; Intrusion Detection System; system security; deep learning; sequential model IoT; Intrusion Detection System; system security; deep learning; sequential model
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MDPI and ACS Style

Zhong, M.; Zhou, Y.; Chen, G. Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods. Sensors 2021, 21, 1113. https://doi.org/10.3390/s21041113

AMA Style

Zhong M, Zhou Y, Chen G. Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods. Sensors. 2021; 21(4):1113. https://doi.org/10.3390/s21041113

Chicago/Turabian Style

Zhong, Ming, Yajin Zhou, and Gang Chen. 2021. "Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods" Sensors 21, no. 4: 1113. https://doi.org/10.3390/s21041113

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