Open Access

ARTICLE

A Hybrid Approach for Network Intrusion Detection

by Mavra Mehmood¹, Talha Javed², Jamel Nebhen³, Sidra Abbas^2,*, Rabia Abid¹, Giridhar Reddy Bojja⁴, Muhammad Rizwan¹

1 Department of Computer Science, Kinnaird College for Women, Lahore, 54000, Pakistan
2 ASET Labs, Islamabad, Pakistan
3 Prince Sattam bin Abdulaziz University, College of Computer Science and Engineering, Alkharj, 11942, Saudi Arabia
4 College of Business and Information Systems, Dakota State University, Madison, United States of America

* Corresponding Author: Sidra Abbas. Email:

(This article belongs to the Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 70(1), 91-107. https://doi.org/10.32604/cmc.2022.019127

Received 03 April 2021; Accepted 12 May 2021; Issue published 07 September 2021

Abstract

Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model's performance. Next, we use various Support Vector Machine (SVM) types to detect intrusion and the Adaptive Neuro-Fuzzy System (ANFIS) to categorize probe, U2R, R2U, and DDOS attacks. The validation of the proposed method is calculated through Fine Gaussian SVM (FGSVM), which is 99.3% for the binary class. Mean Square Error (MSE) is reported as 0.084964 for training data, 0.0855203 for testing, and 0.084964 to validate multiclass categorization.

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Citations