A Novel Hybrid Intrusion Detection Framework Leveraging Machine Learning and Flower Pollination Optimization

Abstract

The exponential growth in technologies such as cloud computing, smart devices, virtualization, and the Internet of Things (IoT) has generated over four hundred zettabytes of network traffic data annually. This surge necessitates robust cybersecurity strategies to protect information from intrusions, which can result in significant financial losses. Reducing security risks requires the use of data analytics and machine learning to derive insights and make informed decisions based on network data. This study introduces the Flower Pollination Optimization (FPO) algorithm for feature selection to enhance the performance of several classifiers on the UNSW-NB15 dataset. We evaluated four classifiers: Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbors (KNN) in two scenarios: without feature selection and with FPO-based feature selection. The results demonstrate significant improvements in classifier performance with FPO, with QDA achieving the highest accuracy of 99.16%. Comparative analysis with recent studies highlights the superior performance of our approach, setting a new benchmark in intrusion detection. This research underscores the essential role of effective feature selection in improving the accuracy and reliability of Intrusion Detection Systems (IDS), particularly in IoT environments.