Application of Synthetic Data Generation Methods to the Detection of Network Attacks on Internet of Things Devices

被引：5

作者：

Ovasapyan, T. D. ^{[1
]}

Danilov, V. D. ^{[1
]}

Moskvin, D. A. ^{[1
]}

机构：

[1] Peter Great St Petersburg Polytech Univ, St Petersburg 195251, Russia

来源：

AUTOMATIC CONTROL AND COMPUTER SCIENCES | 2021年 / 55卷 / 08期

关键词：

generative adversarial network; machine learning methods; Internet of Things; cyberphysical systems; intrusion detection system; ARTIFICIAL NEURAL-NETWORK; SECURITY; THREATS;

D O I：

10.3103/S0146411621080241

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The application of synthetic data generation methods to augment the dataset and subsequently improve the accuracy of detecting network attacks on Internet of Things (IoT) devices using machine learning methods is considered. Augmentation and generative adversarial network methods are used to augment the initial dataset. The IOT Network Intrusion Dataset, which includes network traffic from different attacks (DoS, MiTM, and scan) as well as attacks by one of the most common botnets in the Internet of Things, Mirai, is used as the initial dataset.

引用

页码：991 / 998

页数：8

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