A Hybrid Fuzzy-Neural Network Approach for Advanced Pattern Recognition and Predictive Analytic

Abstract

Organizations must address cybersecurity as a fundamental issue in the digital era, where they face advanced and continuous cyber threats. Intrusion detection systems based on traditional methods face challenges in multiple traffic classification because they enforce static threshold boundaries and possess restricted learning capabilities.

This research envisions a new hybrid fuzzy-neural network solution for sophisticated pattern recognition and predictive analysis in intrusion detection. The main goal is to improve detection accuracy and lower false positives by merging the subtle reasoning of fuzzy logic with the adaptability of learning in neural networks.

The model is tested on the NSL-KDD dataset, which offers extensive labeling of normal and anomalous network traffic. The most important preprocessing steps—encoding categorical variables, scaling numerical data, and dividing the dataset into training and test sets—are performed to make the data appropriate for analysis. A fuzzy logic module assigns a risk score to each traffic record using predefined rules based on impactful features (e.g., src_bytes, dst_bytes, same_srv_rate, diff_srv_rate). Then, the classifications are refined by a Multi-Layer Perceptron (MLP) neural network. The architecture of the network is tuned through grid search and cross-validation, while its performance is evaluated based on metrics like accuracy, ROC-AUC, precision, recall, and F1-score, together with visualization tools like t-SNE.

The hybrid approach achieved a competitive accuracy of 91.7% with an area under the curve (AUC) of 0.98. Analysis of the confusion matrix indicated a high match between actual and predicted labels with low false positive rates. Additionally, t-SNE visualization confirmed clear separation between anomalous and normal traffic, supporting the model's ability to efficiently handle uncertainty and borderline cases.

The combination of fuzzy logic and neural networks within this hybrid solution significantly improves intrusion detection performance through enhanced detection accuracy and decreased false positives. This model presents an encouraging, flexible solution for real-world IDS use, capable of addressing the complexities of today's network environments.

This work contributes by creating a single hybrid framework that combines fuzzy logic with neural networks to efficiently handle fuzzy traffic cases in IDS. It also proposes optimized parameter tuning and an experimental design that guarantees strong performance and generalizability. Lastly, the work experimentally verifies the method on the NSL-KDD dataset, achieving 91.7% accuracy and an AUC of 0.98, while t-SNE visualization effectively distinguishes normal from anomalous traffic.