Predicting Cybersecurity Risk Through Fuzzy and Adaptive Neuro-Fuzzy Systems

As modern networks grow in intricacy, the cybersecurity risks involved are becoming increasingly difficult to foresee and handle. The dynamic, uncertain, and nonlinear nature of these risks cannot be coped with using traditional rule-based and static risk assessment methods. We suggest a Hybrid Cybersecurity Risk Prediction Model through the use of both Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for appropriate prediction as well as risk assessment. This is because FIS tends to offer an interpretable approach towards risk assessment using expert-defined rules, thereby making predictions more comprehensive. On the other hand, the incorporation of the neural network in ANFIS makes predictions and risk assessment accurate. Our model extracts the critical cybersecurity parameters, comprising Authentication Strength, Traffic Anomaly Levels, and Network Load Conditions, from the UNSW-NB15 dataset. Experimental results show that the hybrid FIS-ANFIS model gives a high prediction accuracy, robustness, and adaptiveness over traditional machine-learning models, including. We present the effectiveness of ANFIS in coping with uncertain and evolving cybersecurity threats as a promising tool for real-time cybersecurity risk management. These results strongly substantiate the proposed model significantly outperforms conventional models in reliably and precisely making predictions about cybersecurity risks within complex network environments.