AN IMPROVED GENETIC ALGORITHM BASED FEATURE SELECTION TECHNIQUE FOR INTRUSION DETECTION IN FOG COMPUTING ENVIRONMENT

Abstract

Feature Selection (FS) is critical for reducing the high dimensionality of data, which negatively impacts the classification performance of machine learning models. In the field of Intrusion Detection (ID), where datasets often consist of thousands of attributes and instances, the prevalent issue of data imbalance poses significant challenges, leading to bias in classification tasks. This highlights the pressing need for intelligent techniques to address these challenges effectively. Genetic Algorithm (GA), a widely used evolutionary optimization algorithm for FS, encounters limitations such as slow convergence and a tendency to settle prematurely on suboptimal solutions due to insufficient exploitation capability. These limitations can adversely affect overall performance. Additionally, conventional techniques like the Synthetic Minority Oversampling Technique, commonly employed to handle data imbalance, risk introducing noisy data points into the feature space. To overcome these issues, this study proposes an improved GA-based FS technique featuring an enhanced mutation operator to bolster its exploitation capabilities and deliver improved performance. Furthermore, Adaboost, a more promising machine learning algorithm, is suggested to effectively address data imbalance challenges. The performance of the proposed model was evaluated using benchmark datasets from the Security Laboratory Knowledge Discovery Dataset (NSL-KDD), employing five performance metrics, including accuracy, F1-score, recall, precision, and execution time. The results show that the proposed method outperforms existing techniques across all metrics while effectively tackling the challenges of high-dimensional data and imbalanced datasets, offering a reliable solution for Intrusion Detection.