5G Networks Security Mitigation Model: An ANN-ISM Hybrid Approach - Rafiq Ahmad Khan; Habib Ullah Khan; Hathal Salamah Alwageed; Hussein Al Hashimi; Ismail Keshta (IEEE Open Journal of the Communications Society)
The advent of Fifth-Generation (5G) networks has introduced significant security challenges due to increased complexity and diverse use cases. Conventional threat models may fall short of addressing these emerging threats effectively. This paper presents a new security mitigation model using artificial neural network (ANN) with interpretive structure modeling (ISM) to improve the 5G network security system. The main goal of this study is to develop a 5G network security mitigation model (5GN-SMM) that leverages the predictive capabilities of ANN and the analysis of ISM to identify and mitigate security threats by providing practices in 5G networks. This model aims to improve the accuracy and effectiveness of security measures by integrating advanced computational practices with systematic modeling. Initially, a systematic evaluation of existing 5G network security threats was conducted to identify gaps and incorporate best practices into the proposed model. In the second phase, an empirical survey was conducted to identify and validate the systematic literature review (SLR) findings. In the third phase, we employed a hybrid approach integrating ANN for real-time threat detection and risk assessment and utilizing ISM to analyze the relationships between security threats and vulnerabilities, creating a structured framework for understanding their interdependencies. A case study was conducted in the last stage to test and evaluate 5GN-SMM. The given article illustrates that the proposed hybrid model of ANN-ISM shows a better understanding and management of the security threats than the conventional techniques. The component of the ANN then comes up with the potential of the security breach with improved accuracy, and the ISM framework helps in understanding the relationship and the priorities of the threats. We identified 15 security threats and 144 practices in 5G networks through SLR and empirical surveys. The identified security threats were then analyzed and categorized into 15 process areas and five levels of 5GN-SMM. The proposed model includes state-of-the-art machine learning with traditional information security paradigms to offer an integrated solution to the emerging complex security issues related to 5G. This approach enhances the capacity to detect threats and contributes to good policy enforcement and other risk-related activities to enhance safer 5G networks.