Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Providing an Intelligent Method for Intrusion Detection in Cloud Computing Environment by Optimizing Neural Network Using Genetic Algorithm

Document Type : Persian Original Article

Authors
Wahab Aminiazar 1
Rasoul Farahi 1
Fatemeh Shatri Aliabad 2
1 Department of Computer Engineering and Information Technology, Mah.C., Islamic Azad University, Mahabad, Iran.
2 Department of Computer Engineering and Information Technology, Khal.C., Islamic Azad University, Khalkhal, Iran.
10.22034/jscit.2025.220288
Abstract
Given the increasing expansion of cloud computing applications in recent decades, the emergence of new challenges in this field is a given. Among these, security has always been a fundamental requirement for utilizing these environments. One of the significant issues in the realm of cloud computing security is intrusion detection, which, if appropriate measures and sufficient accuracy are not taken, can lead to risks such as data breaches and potential misuse. Therefore, accurate intrusion detection and enhancing the efficiency of protective systems are of special importance. Over the years, researchers have made extensive efforts to optimize this challenge. However, many existing methods have faced the problem of relatively low detection accuracy. One of the contributing factors to this issue is the heterogeneity among irrelevant and redundant information features in the datasets. This article presents an innovative solution for intrusion detection in cloud infrastructure, relying on an improved neural network classification based on genetic algorithms. This approach aims to improve detection accuracy. In this regard, the principal component analysis (PCA) algorithm has been used initially to reduce the dimensions of the dataset. Then, by utilizing artificial neural network classification, normal traffic is separated from malicious traffic. Subsequently, the parameters of the artificial neural network are optimized using a genetic algorithm. Finally, the performance of the proposed method is evaluated and tested on the standard and comprehensive NSL-KDD dataset, as an indicator for assessing intrusion detection systems, in the MATLAB environment, and compared with the results obtained from three other prominent methods. The results of this comparison show that the proposed method has demonstrated better performance with a detection accuracy of 99.41% compared to other methods and is capable of identifying attacks with higher precision.
Keywords
Intrusion Detection
Cloud Computing
Attacks
Neural Network
Genetic Algorithm
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Journal of Soft Computing and Information Technology
Volume 14, Issue 1
Spring - June
Spring 2025
Pages 45-56