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Review

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

1
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia
2
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia
3
Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, Kota Bharu 16100, Kelantan, Malaysia
*
Authors to whom correspondence should be addressed.
Academic Editors: Dimitrios S. Paraforos and Anselme Muzirafuti
Appl. Sci. 2021, 11(18), 8383; https://doi.org/10.3390/app11188383
Received: 13 August 2021 / Revised: 3 September 2021 / Accepted: 7 September 2021 / Published: 9 September 2021
(This article belongs to the Special Issue Remote Sensing Applications and Agricultural Automation)
The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction. View Full-Text
Keywords: systematic literature review; anomaly intrusion detection; deep learning; IoT; resource constraint; IDS systematic literature review; anomaly intrusion detection; deep learning; IoT; resource constraint; IDS
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MDPI and ACS Style

Alsoufi, M.A.; Razak, S.; Siraj, M.M.; Nafea, I.; Ghaleb, F.A.; Saeed, F.; Nasser, M. Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review. Appl. Sci. 2021, 11, 8383. https://doi.org/10.3390/app11188383

AMA Style

Alsoufi MA, Razak S, Siraj MM, Nafea I, Ghaleb FA, Saeed F, Nasser M. Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review. Applied Sciences. 2021; 11(18):8383. https://doi.org/10.3390/app11188383

Chicago/Turabian Style

Alsoufi, Muaadh A., Shukor Razak, Maheyzah M. Siraj, Ibtehal Nafea, Fuad A. Ghaleb, Faisal Saeed, and Maged Nasser. 2021. "Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review" Applied Sciences 11, no. 18: 8383. https://doi.org/10.3390/app11188383

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