Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT

TL;DR

This paper introduces a hierarchical security architecture and a Markov process-based model to predict and detect FDIA and DDoS attacks in 5G-enabled IoT networks, enhancing security management across stakeholders.

Contribution

It proposes a novel hierarchical security framework and a Markov stochastic process model specifically designed for attack prediction and detection in 5G IoT environments.

Findings

Effective detection of FDIA and DDoS attacks demonstrated through simulations

The Markov model accurately predicts attack behaviors in 5G IoT networks

The architecture improves security management among diverse 5G stakeholders

Abstract

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.