Machine learning to combat cyberattack: a survey of datasets and challenges

Arvind Prasad and Shalini Chandra

The Journal of Defense Modeling and Simulation, 2023, vol. 20, issue 4, 577-588

Abstract: The ever-increasing number of multi-vector cyberattacks has become a concern for all levels of organizations. Attackers are infecting Internet-enabled devices and exploiting them to carry out attacks. These devices are unwittingly becoming part of carrying out cyberattacks. Many studies have proposed machine learning–based promising solutions to stamp out cyberattacks preemptively. We review the machine learning techniques and highlight some promising solutions in recent studies. This study provides the advantage of experimenting with the developed solutions on modern datasets. This survey aims to provide an insightful organization of current developments in cybersecurity datasets and give suggestions for further research. We identified the most frightful cyberattacks and suitable datasets having records related to the attack. This paper discusses modern datasets such as CICIDS2017, CSE-CIC-IDS-2018, CIC-DDoS2019, UNSW-NB15, UNSW-TonIOT, UNSW-BotIoT, DoHBrw2020, and ISCX-URL-2016, which include records of recent sophisticated cyberattacks. This paper will focus on these modern datasets, retrieve detailed knowledge, and experiment with the most commonly used machine learning algorithms. We identify datasets as a significant centric topic that can be addressed with innovative machine learning approaches and solutions to defend against cyberattacks.

Keywords: Cybersecurity; cyberattacks; dataset; DDoS; botnet; reflection-based attack; volumetric attack; machine learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:joudef:v:20:y:2023:i:4:p:577-588

DOI: 10.1177/15485129221094881

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