Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review
Alsoufi, Muaadh and Razak, Shukor and Md Siraj, Maheyzah and Nafea, Ibtehal and Ghaleb, Fuad and Saeed, Faisal and Nasser, Maged (2021) Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review. Applied Sciences, 11 (18). p. 8383. ISSN 2076-3417
|
Text
applsci-11-08383-v2.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) |
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.
| Item Type: | Article | ||||||
|---|---|---|---|---|---|---|---|
| Identification Number: | https://doi.org/10.3390/app11188383 | ||||||
| Dates: |
|
||||||
| Uncontrolled Keywords: | systematic literature review; anomaly intrusion detection; deep learning; IoT; resource constraint; IDS | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence |
||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Faisal Saeed | ||||||
| Date Deposited: | 05 Jan 2022 14:05 | ||||||
| Last Modified: | 05 Jan 2022 14:05 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/12585 |
Actions (login required)
 |
View Item |
Tools
Tools