The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks
Open accessAbstract
Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the most lethal cyber-attacks that can target an UAV network, namely, false information dissemination, GPS spoofing, jamming, and black hole and gray hole attacks. Extensive simulations confirm that the proposed scheme performs well in terms of attack detection even with a large number of UAVs and attackers since it exhibits a high detection rate, a low number of false positives, and prompt detection with a low communication overhead.
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Cites
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Cited by
Security analysis of drones systems: Attacks, limitations, and recommendations
Yaacoub, J.-P., Noura, H., Salman, O., & Chehab, A. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100218 · Cited by 727
Blockchain envisioned uav networks: challenges, solutions, and comparisons
Mehta, P., Gupta, R., & Tanwar, S. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.01.023 · Cited by 266
Federated learning-based cognitive detection of jamming attack in flying Ad-Hoc network
Mowla, N. I., Tran, N. H., Doh, I., & Chae, K. (2020) · IEEE Access · doi:10.1109/access.2019.2962873 · Cited by 147
AFRL: Adaptive federated reinforcement learning for intelligent jamming defense in FANET
Mowla, N. I., Tran, N. H., Doh, I., & Chae, K. (2020) · Journal of Communications and Networks · doi:10.1109/jcn.2020.000015 · Cited by 146
Metadata
- Authors
- Sedjelmaci, H., Senouci, S. M., & Ansari, N.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2018-09
- Volume
- 48
- Issue
- 9
- Pages
- 1594-1606
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2601260651
- Cited by
- 282
- References
- 37
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- green
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- Sedjelmaci, H., Senouci, S. M., & Ansari, N. (2018). A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2017.2681698
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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