Muhammad Arief, Made Gunawan, Agung Septiadi, Mukti Wibowo, V. Pragesjvara, Kusnanda Supriatna, Anto Satriyo Nugroho, Gusti Bagus, B. Nugraha, S. Supangkat
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引用次数: 0
摘要
要生成具有良好性能的机器学习(ML)和深度学习(DL)架构,我们需要一个合适的数据集,用于开发过程中的训练和测试阶段。从知识发现和数据挖掘(KDD)杯 99 数据集开始,自 1998 年以来,已经有许多数据集被用于基于 ML 和 DL 的入侵检测系统(IDS)的训练和测试过程。由于可访问的数据集非常多,研究人员在选择使用哪个数据集时可能会遇到困难。因此,随着新数据集的定期创建,评估数据集是否适合要开展的研究的框架变得越来越重要。此外,鉴于近年来物联网(IoT)设备的日益普及以及物联网特定数据集的不断增加,为物联网数据集建立一个特定的框架至关重要。因此,本研究旨在为基于 ML 和 DL 的 IDS 开发一个评估物联网数据集的新框架。研究成果包括:第一,用于评估物联网数据集的新型框架;第二,将该新型框架与其他现有框架进行比较;第三,使用该新型框架对五个物联网数据集进行分析。
A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems
To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.