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

共 23 条

[1]

Belenko V., 2018, INT RUSS AUT C RUSAU, P1

[2] Synthetic datasets generation for intrusion detection in VANET [J].

Belenko, Viacheslav ;

Krundyshev, Vasiliy ;

Kalinin, Maxim .

11TH INTERNATIONAL CONFERENCE ON SECURITY OF INFORMATION AND NETWORKS (SIN 2018), 2018,

[3]

Cho H., ADV SUSTAINABILITY S, V2021, P235, DOI [10.1007/978-981-33-4901-8_14, DOI 10.1007/978-981-33-4901-8_14]

[4] Inverting the Generator of a Generative Adversarial Network [J].

Creswell, Antonia ;

Bharath, Anil Anthony .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (07) :1967-1974

[5] Analysis of the Information Security Threats in the Digital Production Networks [J].

Dakhnovich, A. D. ;

Moskvin, D. A. ;

Zegzhda, D. P. .

AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2018, 52 (08) :1071-1075

[6] Threat Analysis of Cyber Security in Wireless Adhoc Networks Using Hybrid Neural Network Model [J].

Demidov, R. A. ;

Zegzhda, P. D. ;

Kalinin, M. O. .

AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2018, 52 (08) :971-976

[7] Application Model of Modern Artificial Neural Network Methods for the Analysis of Information Systems Security [J].

Demidov, R. A. Z. ;

Pechenkin, A. I. ;

Zegzhda, P. D. ;

Kalinin, M. O. .

AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2018, 52 (08) :965-970

[8] Distributed attack detection scheme using deep learning approach for Internet of Things [J].

Diro, Abebe Abeshu ;

Chilamkurti, Naveen .

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 82 :761-768

[9]

Fimichev N.N, 2015, SOVR NAUCH ISSLED IN, P91

[10] Sequence Alignment Algorithms for Intrusion Detection in the Internet of Things [J].

Kalinin, M. ;

Krundyshev, V .

NONLINEAR PHENOMENA IN COMPLEX SYSTEMS, 2020, 23 (04) :397-404

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