Improving Intrusion Detection Systems Using the Human Evolutionary Optimization Algorithm - Koochaki, Mohammad; Sahafi, Amir (Management Strategies and Engineering Sciences, 2027)
An intrusion detection system is a critical tool in network security that identifies potential threats by analyzing behavioral patterns and network traffic. Despite recent advances, challenges such as imbalanced data, high feature dimensionality, and a high false-positive alarm rate remain persistent. This study aimed to improve the performance of intrusion detection systems by using the Human Evolutionary Optimization Algorithm (HEOA) for selecting effective features and by tuning the hyperparameters of the XGBoost model through a genetic algorithm. Inspired by human behaviors, HEOA has a strong capacity for rapid convergence and avoidance of local optima. The present study was conducted on two standard datasets, NSL-KDD and CIC-IDS 2017, and the preprocessing steps included normalization, encoding, and five-fold cross-validation. The evaluation results showed that the proposed HEOA-XGBoost model achieved higher accuracy and F1-score and produced a lower false-positive rate compared with conventional methods. These findings indicate the positive effect of using HEOA in improving the efficiency of machine learning models for intrusion detection. The main innovation of this study lies in applying HEOA to optimize feature selection and using a genetic algorithm for hyperparameter tuning, which enhances the accuracy and stability of intrusion detection systems.