Data Science in Cybersecurity to Detect Malware-Based Domain Generation Algorithm: Improvement, Challenges, and Prospects

Mohamed Hassaoui; Mohamed Hanini; Said El Kafhali

doi:10.47852/bonviewJCCE42022875

Authors

Mohamed Hassaoui Computer, Networks, Modeling, and Mobility Laboratory (IR2M), Hassan First University of Settat, Morocco https://orcid.org/0000-0002-1809-8259
Mohamed Hanini Computer, Networks, Modeling, and Mobility Laboratory (IR2M), Hassan First University of Settat, Morocco https://orcid.org/0000-0001-8998-9617
Said El Kafhali Computer, Networks, Modeling, and Mobility Laboratory (IR2M), Hassan First University of Settat, Morocco https://orcid.org/0000-0001-9282-5154

DOI:

https://doi.org/10.47852/bonviewJCCE42022875

Keywords:

domain generation algorithms, data science, malware, cybersecurity, machine learning

Abstract

Nowadays, the malware communicates with command and control servers using domains generated algorithmically. Domain generation algorithms (DGAs) are continually evolving, which degrades the accuracy of the existing methods calls for the continuous tracking of how DGAs develop and their detection methods and calls for a good evaluation of the stage to open horizons for new detection methods. Data science plays a key role in cybersecurity by providing methods for detecting and analyzing network traffic data, including DGAs, and helping to improve the overall security of computer systems and networks. It can also be used to analyze large datasets of domain names and to develop and optimize solutions for DGA detection, by applying techniques such as machine learning, deep learning, and genetic algorithms, which have shown their effectiveness in detecting new and unknown DGAs. This paper reviews the role of data science in cybersecurity systems to detect DGAs. Hence, it also brings together publicly available domain name datasets and data science techniques utilized in recent DGA detection systems to highlight current issues and potential directions. This article additionally explains issues related to DGA detection. This will assist researchers in improving the current DGA detection algorithms as well as creating new powerful models.

Received: 21 March 2024 | Revised: 6 May 2024 | Accepted: 21 May 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.