Improving the Efficiency of an Intrusion Detection System Using a Decision Tree Adjusted with an Improved Random Fractal Search Combined with the Whale Otimization Algorithm

Subject Areas : New technologies in distributed systems and algorithmic computing

شهرزاد رحیمی ¹ , Masood Niazi Torshiz ² , Seyyed Abed Hosseini ³

1 - دانشگاه ازاد مشهد
2 - Department of Computer Engineering, Ma.C., Islamic Azad University, Mashhad, Iran
3 - Department of Electrical Engineering, Ma.C., Islamic Azad University, Mashhad, Iran

Received: 2025-10-05 Accepted : 2025-11-26 Published : 2025-12-21

Keywords: Intrusion detection system, Random fractal search algorithm, Chaotic mapping, Whale optimization algorithm, Clustering,

Abstract :

With the increasing use of the Internet, a large amount of information is exchanged between different communication devices. Data must be securely transmitted between communication devices, and therefore, network security is one of the dominant research areas for the current network scenario. Intrusion detection systems are widely used in conjunction with other security mechanisms such as firewalls and access control. On the other hand, the evolution in attack scenarios has been such that finding efficient and optimal intrusion detection systems with frequent updates has become a big challenge. Implementation of an intrusion detection system using machine learning techniques and updated intrusion data sets is one of the effective modeling solutions for an intrusion detection system. In this article, an improved random fractal search algorithm with chaos maps is introduced. To improve the mining ability of this algorithm, the relationships of the whale algorithm have been used in combination with it. This algorithm has been used to cluster infiltrated and normal data. Then, a decision tree classifier was used to classify the infiltrated data on the NSL-KDD dataset. To evaluate the proposed method, its results have been compared with the case without clustering. In terms of the error and sensitivity criteria for the test data, the proposed method is equal to 0.2211, 0.5816, and the decision tree without clustering is equal to 0.2477, 0.5692, respectively. In terms of specificity and accuracy criteria, the proposed method has obtained better results compared to the decision tree without clustering. Therefore, the results showed that the proposed method has better efficiency and performance compared to the decision tree method without clustering.

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