An Extensive Survey on Intrusion Detection Systems: Datasets and Challenges for Modern Scenario

Vanlalruata Hnamte, J. Hussain
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引用次数: 7

Abstract

Cyberattacks are becoming more and more advanced, making it more difficult to identity suspicious activities on network traffic. Weaponizing the data in the line between network attacks and data breaches continues and the number rises upward even during the recent year with a massive increase in the attack type. Many consider Intrusion Detection System (IDS) datasets publicly available are becoming outdated and inadequate due to the availability of newer attack techniques. Therefore, it is a concern that the extensive usage of these available datasets in the current attack scenario to evaluate IDS models. This paper lists 37 datasets available for testing the IDS models and discusses those publicly accessible datasets, describing the characteristics and limitations for researchers who use such datasets. Finally, based on the dataset characteristics and usage survey, we conclude with a summary of the problems and provide our insights and suggestions for the use of network-based datasets for the Deep Learning approach for further improvement.
入侵检测系统的广泛调查:现代场景的数据集和挑战
网络攻击变得越来越先进,使得识别网络流量中的可疑活动变得更加困难。将网络攻击和数据泄露之间的数据武器化仍在继续,即使在最近几年,随着攻击类型的大幅增加,数量也在上升。许多人认为,由于新攻击技术的出现,公开可用的入侵检测系统(IDS)数据集变得过时和不足。因此,在当前攻击场景中广泛使用这些可用数据集来评估IDS模型是一个值得关注的问题。本文列出了37个可用于测试IDS模型的数据集,并讨论了这些可公开访问的数据集,描述了使用这些数据集的研究人员的特征和限制。最后,根据数据集的特点和使用情况调查,总结了存在的问题,并为基于网络的数据集在深度学习方法中的使用提供了我们的见解和建议,以进一步改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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