A novel optimized probabilistic neural network approach for intrusion detection and categorization

Nowadays, the web provides all of the nation's daily necessities, and time spent online is rising quickly. The Internet is being used more widely than ever. As a result, cyberattacks and cybercrime are becoming more prevalent. Various machine learning techniques will be used to rec-ognize network attacks and defend against cyber security threats. Developing intrusion detection systems can improve cybersecurity and identify anomalies on a computer server. An efficient intru-sion detection and prevention system will be created using machine learning techniques. Each intru-sion detection categorization system evaluated in this study has its unique uses. The Firefly Optimization (FFO) technique was used to detect the intrusions before the categorization proce-dure was carried out using a machine learning classifier. It considered how the anomalies in net-works were categorized in this research. The outcomes of the detection techniques will be validated using the Knowledge Discovery Dataset (KDD-CUP 99). The proposed method involves Probabilistic Neural Network for the categorization. The implementation will assess many perfor-mance metrics for various cyber-attack types, including specificity, recall, F1-score, accuracy, pre-cision, and sensitivity. The proposed technique achieves a high accuracy of 98.99%.& COPY; 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).