Attack Detection in Fog Layer for IIoT Based on Machine Learning Approach

被引：0

作者：

Maharani, Mareska Pratiwi ^{[1
]}

Daely, Philip Tobianto ^{[1
,2
]}

Lee, Jae Min ^{[1
]}

Kim, Dong-Seong ^{[1
]}

机构：

[1] Kumoh Natl Inst Technol, Dept IT Convergence Engn, Gumi, South Korea

[2] Inst Teknol Telkom Surabaya, Dept Informat Technol, Surabaya, Indonesia

来源：

11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020) | 2020年

关键词：

Attack detection; fog computing; IIoT; KDD Cup'99 dataset; machine learning; INTERNET;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In Industrial internet of things(IIoT), the infrastructure and the technology has been improved a lot throughout times. With those improvements, the threats and attacks are also growing rapidly to become more various and advanced attacks. One of the weakest parts in IIoT infrastructure is in the cloud layer that can cause the system failure, but it can reduce the possibility by controlling and maximizing the ability in the fog layer as its near to the edge of devices. In this paper, attack detection in fog computing framework with several machine learning algorithms to efficiently detecting malicious activities is proposed. The evaluation performed by using KDD Cup'99 dataset and compared by using Decision Tree, K-Means, and Random Forest algorithms.

引用

页码：1880 / 1882

页数：3

共 10 条

[1]

Alrashdi I, 2019, 2019 IEEE 10TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON), P515, DOI [10.1109/uemcon47517.2019.8992963, 10.1109/UEMCON47517.2019.8992963]

[2]

[Anonymous], 2017, P 2017 4 INT C SIGNA

[3]

Bhatt P, 2017, IEEE GLOB HUMANIT C, P81

[4] Network intrusion detection based on random forest and support vector machine [J].

Chang, Yaping ;

Li, Wei ;

Yang, Zhongming .

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1, 2017, :635-638

[5] Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices [J].

Eskandari, Mojtaba ;

Janjua, Zaffar Haider ;

Vecchio, Massimo ;

Antonelli, Fabio .

IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (08) :6882-6897

[6] IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices [J].

Meneghello, Francesca ;

Calore, Matteo ;

Zucchetto, Daniel ;

Polese, Michele ;

Zanella, Andrea .

IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (05) :8182-8201

[7]

Panchal AC, 2018, IEEE GLOB CONF WIREL, P124, DOI 10.1109/GCWCN.2018.8668630

[8] Design of Cognitive Fog Computing for Intrusion Detection in Internet of Things [J].

Prabavathy, S. ;

Sundarakantham, K. ;

Shalinie, S. Mercy .

JOURNAL OF COMMUNICATIONS AND NETWORKS, 2018, 20 (03) :291-298

[9]

Ramli MR, 2019, IEEE INT C EMERG, P1661, DOI [10.1109/etfa.2019.8869402, 10.1109/ETFA.2019.8869402]

[10] MSML: A Novel Multilevel Semi-Supervised Machine Learning Framework for Intrusion Detection System [J].

Yao, Haipeng ;

Fu, Danyang ;

Zhang, Peiying ;

Li, Maozhen ;

Liu, Yunjie .

IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (02) :1949-1959

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