Paper Info
Title
Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT
Abstract
Security in fifth generation (5G) networks has become one of the prime concerns in the telecommunication industry. 5G security challenges come from the fact that 5G networks involve different stakeholders using different security requirements and measures. Deficiencies in security management between these stakeholders can lead to security attacks. Therefore, security solutions should be conceived for the safe deployment of different 5G verticals (e.g., industry 4.0, Internet of Things (IoT), and so on). The interdependencies among 5G and fully connected systems, such as IoT, entail some standard security requirements, namely integrity, availability, and confidentiality. In this article, we propose a hierarchical architecture for securing 5G enabled IoT networks, and a security model for the prediction and detection of False Data Injection Attacks (FDIA) and Distributed Denial of Service attacks (DDoS). The proposed security model is based on a Markov stochastic process, which is used to observe the behavior of each network device, and employ a range-based behavior sifting policy. Simulation results demonstrate the effectiveness of the proposed architecture and model in detecting and predicting FDIA and DDoS attacks in the context of 5G enabled IoT.
Year
DOI
Venue
2021
10.1109/MNET.011.2000449
IEEE Network
Keywords
DocType
Volume
security management,security attacks,security solutions,different 5G verticals,fully connected systems,standard security requirements,IoT networks,security model,FDIA,network device,DDoS attacks,fifth generation networks,prime concerns,telecommunication industry,false data injection attacks
Journal
35
Issue
ISSN
Citations 
2
0890-8044
3
PageRank 
References 
Authors
0.38
0
3
Name
Order
Citations
PageRank
Hajar Moudoud181.80
Lyes Khoukhi230444.30
Soumaya Cherkaoui318740.89