A Hybrid Approach for Intrusion Detection in Computer Systems Using Optimized Deep Neural Networks

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 CICDDOS۲۰۱۹ database, containing ۵۴,۰۰۰ samples and ۲۲ 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 ۰.۰۱۱۷ and an accuracy of ۹۶.۳۲% performs better than other algorithms.

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