With the ubiquitous nature of information technology solutions that facilitate communication in the modern world, cyber attacks are increasing in volume and becoming more sophisticated in nature. From classic network-based Denial of Service (DoS) attacks to the more recent concerns of privacy compromises, Intrusion Detection Systems (IDS) are becoming an urgent need to safeguard the modern information technology landscape. The development of these IDS relies on training and evaluation datasets that must evolve with time and represent the contemporary threat landscape. The purpose of this analysis is to explore such recent datasets, describe how they enable research endeavours and the development of novel IDS. Specifically, 7 recent datasets published for IDS research have been reviewed along with selected publications that have employed them. In doing so, the discussion emphasizes the need for the publication of even more modern datasets, especially for emerging technologies such as the Internet of Things (IoT) and smartphone devices, to ensure that modern networks and communication channels are secured. Furthermore, a taxonomy based on the discussed datasets has been developed that can be used to inform the dataset selection process for future research endeavours.
翻译:随着便利现代世界通信的信息技术解决方案的无处不在的性质,网络攻击的数量正在增加,性质越来越复杂。从传统的基于网络的拒绝服务(DoS)攻击到最近对隐私妥协的关切,入侵探测系统(IDS)正在成为保护现代信息技术景观的迫切需要。这些信息传输系统的开发依赖于培训和评估数据集,这些数据必须随着时间的演变而变化,并代表当代威胁景观。本分析的目的是探索这种最近的数据集,描述它们如何促进研究工作和开发新的ISD。具体地说,为IDS研究公布的7个最新数据集已经与使用这些数据集的某些出版物一起得到审查。在这样做时,讨论强调需要出版更现代的数据集,特别是诸如Things Internet(IoT)和智能手机装置等新兴技术,以确保现代网络和通信渠道的安全。此外,基于讨论过的数据集的分类已经发展,可以用来为未来研究工作的数据集选择进程提供信息。