Abstract
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.
Highlights
IntroductionInternet of Things (IoT) is the research and industrial trend in the arena of InformationCommunications Technology (ICT) that has become accustomed to being part of technology advancement in our everyday life [1]
Internet of Things (IoT) is the research and industrial trend in the arena of InformationCommunications Technology (ICT) that has become accustomed to being part of technology advancement in our everyday life [1]
This study presented a systematic review of anomaly Intrusion Detection System (IDS) in IoT environments using deep learning
Summary
Internet of Things (IoT) is the research and industrial trend in the arena of InformationCommunications Technology (ICT) that has become accustomed to being part of technology advancement in our everyday life [1]. Existing deep learning studies related to IoT security focus primarily on experimental aspects rather than the adopted techniques, leaving a gap for a comprehensive review of different anomaly intrusion detection. For such reason, the goals are to identify what is the most prominently used techniques and how to ensure better performances for each technique. The finding of our work inspired us to propose this work, which is an in-depth systematic literature review following the guideline based on proposed work by Kitchenham to provide researchers and developers in-depth information and obtain details about an up-to-date technique and methodology in anomaly intrusion detection in IoT, using deep learning [25]
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