Home > Published Issues > 2020 > Volume 15, No. 11, November 2020 >

Lightweight Convolutional Neural Network Based Intrusion Detection System

Vinh Pham, Eunil Seo, and Tai-Myoung Chung
Sungkyunkwan University, Suwon and 16419, South Korea

Abstract—Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.
Index Terms—Intrusion detection, machine learning, convolutional neural network

Cite: Vinh Pham, Eunil Seo, and Tai-Myoung Chung, "Lightweight Convolutional Neural Network Based Intrusion Detection System," Journal of Communications vol. 15, no. 11, pp. 808-817, November 2020. Doi: 10.12720/jcm.15.11.808-817

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.