Predicting Cybersecurity Risk Through Fuzzy and Adaptive Neuro-Fuzzy Systems

P Bhanu Prakash; B Sivani; S Burhan; B Siva Gopi Chandu; K Sumana

doi:10.5281/zenodo.18755919

Authors

P Bhanu Prakash Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
B Sivani Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
S Burhan Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
B Siva Gopi Chandu Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
K Sumana Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.

DOI:

https://doi.org/10.5281/zenodo.18755919

Keywords:

Cybersecurity Risk Prediction, Adaptive Neuro-Fuzzy Inference System (ANFIS), Fuzzy Logic, Machine Learning, Intrusion Detection, Risk Assessment

Abstract

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.

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