Network traffic classification using convolutional neural network and ant-lion optimization. (July 2022)

• Record Type: Journal Article
• Authors: Izadi, Saadat; Ahmadi, Mahmood; Nikbazm, Rojia
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Network traffic classification -- Traffic identification -- Ant-lion meta-heuristic algorithm -- Convolutional neural network -- Self-organizing map; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108024
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Traffic identification has become a challenging task in recent years. Recently, deep learning methods have been extensively studied for network traffic classification recently. Unfortunately, these models require a large amount of training data. Another challenge with most traffic classification methods is that the features must be extracted by an expert. In these methods, finding the desired features that lead to a better classification is very tedious and time-consuming. In this regard, an excellent solution to address this challenge is to use of deep learning methods that automatically extract features. This paper is an attempt to address these issues by using a combination of the convolutional neural network (CNN), the ant-lion meta-heuristic algorithm (ALO), and the self-organizing map (SOM) to create a traffic classification model that can accurately identify traffic types. The proposed method was able to identify encrypted traffic and distinguish between VPN and non-VPN traffics. The model was evaluated using the "ISCX VPN-non-VPN" dataset, and achieved 98% accuracy. Graphical abstract: Highlights: Preprocessing traffic data using entropy and entropy variance. Automated feature extraction from the hidden layers of the one-dimensional convolutional neural network (1D-CNN). Efficient feature selection with high classification accuracy using Ant-lion optimization. Classification of new instances using a fuzzy-SOM based-clustering.