A new Approach to Detecting Intrusion and Malicious Behaviors in Big Data

Subject Areas : Electrical and Computer Engineering

Homa Movahednejad ¹ , Mohsen Porshaban ² , Ehsan Yazdani Chamzini ³ , Elahe Hemati Ashani ⁴ , Mahdi Sharifi ⁵

1 - Faculity of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Faculity of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 - Faculity of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
4 - Faculity of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
5 - Faculity of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Received: 2024-01-03 Accepted : 2024-04-23 Published : 2025-03-19

Keywords: Intrusion detection, Malicious behavior, Support vector machine algorithm, Big data, Bacterial foraging optimization algorithm,

Abstract :

Today, maintaining information security and intrusion detection is very important to deal with malicious behaviors in massive data. In this article, a hybrid method for detecting malicious data is presented wherein three factors of time progress, history of users and scalability are taken into account. The proposed method utilizes storage and feature extraction techniques to increase the speed and reduce the amount of calculations. In addition, the support vector machine algorithm has been modified for classification, and the parallelized bacterial foraging optimization algorithm has been used for feature extraction. The results show that the proposed algorithm outperforms the existing methods in terms of detection rate by 21%, false positive rate by 62%, accuracy by 15% and execution time by 70%. The reduction in execution time indicates that less energy is needed to run the algorithm which results in saving energy and can be beneficial for use in green energy systems.

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