TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

Real time malware detection in encrypted network traffic using machine learning with time based features

* ,

* Corresponding author · click or hover a name for details

pp. 841–850Vol. 26Issue 3April 2023DOI: 10.47974/JDMSC-1760 Crossmark XML
Published Online:
01 Apr 2023
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-1760
Pages:
841–850

Abstract

With the increasing amount of Internet users, malware attacks are also growing. The purpose of malicious authors creating malware is to attack, damage, or impair electronic devices. In recent times, malware authors are also using HTTPs traffic; therefore, detecting malware in HTTPs traffic is intriguing since network traffic is enciphered. As the network traffic is enciphered, it is an arduous job to identify benign and malicious traffic. It also poses a significant challenge for firewalls and anti-malware software. Hence, it is essential to monitor the network traffic for detecting malware and threats in this way that maintains the encryption integrity. In this paper, a machine learning based model was proposed, which can effectively and efficiently detect malware without deciphering the network traffic. The prime objective of the research work is to apply several of ML techniques to detect malware in real-time utilizing time-based features. The proposed methodology can classify malware attacks in less than one second, achieving an accuracy of 99% on the Central Processing Unit (CPU) and Graphics Processing Unit (GPU) platform, which is sufficient for detecting malware in real-time.

Keywords

MalwareTime based featuresMachine learningNetwork trafficReal time detection

Subject Classifications

68M1268M25

References

[1] Yask and B. S. Kumar, “Construct the Filter for Malware Detection in Distributed Control System,” Journal of Information and Optimization Sciences, Vol. 40, No. 5, pp. 1155-1166 (2019).
[2] Let’s encrypt stats. URL https://letsencrypt.org/stats/#percent-pageloads, Last Accessed, 2021.11.21.
[3] Arna Magnúsardóttir. Malware is Moving Heavily to Https. https://www.cyren.com/blog/articles/over-one-third-of-malware-uses-https, Last Accessed, 2018.10.19.
[4] Kohut, Jan, Tomáš Komárek, Přemysl Čech, Jan Bodnár, and Jakub Lokoč. “Learning Communication Patterns for Malware Discovery in HTTPs Data”, Expert Systems with Applications, Vol.101, pp. 129-142 (2018).
[5] M. J. Erquiaga and S. Garcia, ‘‘Malware Capture Facility Project,’’ https://mcfp.weebly.com, CVUT University, 2013
[6] CICFlowmeter, https://www.unb.ca/cic/research/applications.html Last Accessed 28.07.2021.
[7] D.B. Bisandu, R. Prasad, and M.M. Liman, “Data Clustering using Efficient Similarity Measures,” Journal of Statistics and Management Systems, Vol. 22, No. 5, pp. 901-922 (2019).
[8] Draper-Gil, Gerard, Arash Habibi Lashkari, Mohammad Saiful Islam Mamun, and Ali A. Ghorbani. “Characterization of Encrypted and VPN Traffic Using Time-Related”, In Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP), pp. 407-414 (2016).
[9] Bagui, Sikha, Xingang Fang, Ezhil Kalaimannan, Subhash C. Bagui, and Joseph Sheehan. “Comparison of Machine-Learning Algorithms for Classification of VPN Network Traffic Flow Using Time-Related Features”, Journal of Cyber Security Technology, Vol. 1, No. 2, pp. 108-126 (2017).
[10] Rezaei, Shahbaz, and Xin Liu. “Deep Learning for Encrypted Traffic Classification: An overview”, IEEE Communications Magazine, Vol. 57, No. 5, pp. 76-81 (2019).
[11] Lopez-Martin, Manuel, Belen Carro, Antonio Sanchez-Esguevillas, and Jaime Lloret. “Network Traffic Classifier with Convolutional and Recurrent Neural Networks for Internet of Things”, IEEE Access, Vol. 5, pp. 18042-18050 (2017).

Views: 488Downloads: 123Citations: 1