The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Anomaly-based intrusion detection system for IoT networks through deep learning model
Abstract
: The Internet of Things (IoT) idea has been developed to enhance people's lives by delivering a diverse range of smart interconnected devices and applications in several domains. However, security threats are main critical challenges for the devices in an IoT environment. Many approaches have been proposed to secure IoT appliances in state of the art, still advancement is desirable. Machine learning has demonstrated a capability to detect patterns when other methodologies have collapsed. One advanced method to enhance IoT security is to employ deep learning. This formulates a seamless option for anomaly-based detection. This paper presents a CNN-based approach for anomaly-based intrusion detection systems (IDS) that takes advantage of IoT's power, providing qualities to efficiently examine whole traffic across the IoT. The proposed model shows ability to detect any possible intrusion and abnormal traffic behavior. The
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Cites
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Deep Learning Approach for Intelligent Intrusion Detection System
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019) · IEEE Access · doi:10.1109/access.2019.2895334 · Cited by 1,946
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
Distributed attack detection scheme using deep learning approach for Internet of Things
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Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks
Abomhara, M., Department of Information and Communication Technology, U. O. A., K�ien, G. M., & Department of Information and Communication Technology, U. O. A. (2015) · Journal of Cyber Security and Mobility · doi:10.13052/jcsm2245-1439.414 · Cited by 662
A Supervised Intrusion Detection System for Smart Home IoT Devices
Anthi, E., Williams, L., Slowinska, M., Theodorakopoulos, G., & Burnap, P. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2926365 · Cited by 597
Intrusion detection systems for IoT-based smart environments: a survey
Elrawy, M. F., Awad, A. I., & Hamed, H. F. A. (2018) · Journal of Cloud Computing · doi:10.1186/s13677-018-0123-6 · Cited by 481
Intrusion detection systems in the Internet of things: A comprehensive investigation
Hajiheidari, S., Wakil, K., Badri, M., & Navimipour, N. J. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.05.014 · Cited by 239
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Malicious Insider Attack Detection in IoTs Using Data Analytics
Khan, A. Y., Latif, R., Latif, S., Tahir, S., Batool, G., & Saba, T. (2020) · IEEE Access · doi:10.1109/access.2019.2959047 · Cited by 116
Self-citation
Cited by
An Adaptive Intrusion Detection System in the Internet of Medical Things Using Fuzzy-Based Learning
Alalhareth, M., & Hong, S.-C. (2023) · Sensors · doi:10.3390/s23229247 · Cited by 52
Devi, M., Nandal, P., & Sehrawat, H. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200553 · Cited by 27
Metadata
- Authors
- Saba, T., Rehman, A., Sadad, T., Kolivand, H., & Bahaj, S. A.
- Venue
- Computers and Electrical Engineering
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 99
- Pages
- 107810
- ISSN
- 0045-7906 (ISSN-L)
- OpenAlex
- W4212986808
- Cited by
- 445
- References
- 18
- FWCI
- 42.79
- Type
- journal article
- Language
- en
- Open access
- closed
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- Saba, T., Rehman, A., Sadad, T., Kolivand, H., & Bahaj, S. A. (2022). Anomaly-based intrusion detection system for IoT networks through deep learning model. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2022.107810
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