The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
A Novel Deep Learning-Based Intrusion Detection System for IoT Networks
Open accessAbstract
The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes the threat of identity protection. Detecting intrusion on IoT devices in real-time is essential to make IoT-enabled services reliable, secure, and profitable. This paper presents a novel Deep Learning (DL)-based intrusion detection system for IoT devices. This intelligent system uses a four-layer deep Fully Connected (FC) network architecture to detect malicious traffic that may initiate attacks on connected IoT devices. The proposed system has been developed as a communication protocol-independent system to reduce deployment complexities. The proposed system demonstrates reliable performance for simulated and real intrusions during the experimental performance analysis. It detects the Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Workhole attacks with an average accuracy of 93.74%. The proposed intrusion detection system’s precision, recall, and F1-score are 93.71%, 93.82%, and 93.47%, respectively, on average. This innovative deep learning-based IDS maintains a 93.21% average detection rate which is satisfactory for improving the security of IoT networks.
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Cited by
A deep learning-based intrusion detection approach for mobile Ad-hoc network
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Metadata
- Authors
- Awajan, A.
- Venue
- Computers
- Publisher
- MDPI AG
- Published
- 2023-02-05
- Volume
- 12
- Issue
- 2
- Pages
- 34
- ISSN
- 2073-431X (ISSN-L)
- OpenAlex
- W4319316768
- Cited by
- 292
- References
- 31
- FWCI
- 32.79
- Type
- journal article
- Language
- en
- Open access
- gold
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