Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT
This addresses security challenges for 5G and IoT stakeholders, but appears incremental as it builds on existing methods like Markov processes for attack detection.
The paper tackles the problem of securing 5G-enabled IoT networks by proposing a hierarchical architecture and a security model based on a Markov stochastic process to predict and detect False Data Injection Attacks (FDIA) and Distributed Denial of Service (DDoS) attacks, with simulation results demonstrating its effectiveness.
Security in the 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), etc.). 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.