Document Type : Original Article

Author

• Maral Kolahkaj

dept. of Computer Engineering Sousangerd Branch, Islamic Azad University Sousangerd, Iran

Abstract

Abstract— The issue of intrusion in security presents a fundamental challenge that can lead to serious damage in IT systems. Intrusion Detection Systems (IDS) serve as effective tools for identifying intrusion activities and generating alerts. However, traditional IDS methods often face issues such as low accuracy and long training times. Therefore, enhancing the performance and efficiency of these systems is crucial. The proposed approach in this study leverages evolutionary optimization algorithms combined with machine learning approaches to improve accuracy and training speed in IDS and better manage large volumes of data. This combination leads to the development of an Evolutionary Neural Network (ENN) that enhances and optimizes IDS performance. In this approach, BUZOA and Ant Colony Optimization (ACO) algorithms are used for feature selection, and decision tree, k-nearest neighbor, support vector machine, and deep neural network algorithms are used for classification and intrusion detection. The dataset used in this research is from the CICDDOS2019 database, containing 54,000 samples and 22 initial features. The experimental results indicate that among the metaheuristic algorithms BUZOA and ACO, and their combinations with decision tree, k-nearest neighbor, and support vector machine, the BUZOA-CNN hybrid algorithm with an average RMSE of 0.0117 and an accuracy of 96.32% performs better than other algorithms.

Keywords

• Keywords— Intrusion Detection
• Cyber Attacks
• BUZOA Optimization Algorithm
• Ant Colony Optimization
• Deep Learning

Main Subjects

• Artificial Intelligence, Robotics, Cognitive Computing