LDoS Attacks Detection for ICPS NB-IoTs Environment via SE-Based CNN

Hsin Hung Cho; Min Yan Tsai; Jiang Yi Zeng; Chia Mu Yu; Han Chieh Chao; Ilsun You

doi:10.1109/JSYST.2023.3302876

LDoS Attacks Detection for ICPS NB-IoTs Environment via SE-Based CNN

Hsin Hung Cho, Min Yan Tsai, Jiang Yi Zeng, Chia Mu Yu, Han Chieh Chao, Ilsun You^*

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

After Industry 4.0 was proposed, cyber-physical systems (CPS) were also introduced into the industrial environment called the industrial CPS. Internet-of-Things (IoT) services in the industry make intelligent decision-making more effective. In large enterprises, there are often requirements for cross-factory and transnational business. Narrowband IoT (NB-IoT) can provide wider coverage and indoor support, making deployment more flexible. However, NB-IoT is not equipped with powerful computing capabilities, preventing NB-IoT devices from having powerful information security software for defense. Thus, NB-IoT is vulnerable to low-rate denial-of-service attacks. Such attacks hide in normal traffic and are difficult to detect. This study uses a novel search economy to improve the weight combination search of a convolutional neural network to achieve a better detection rate.

Original language	English
Pages (from-to)	5280-5291
Number of pages	12
Journal	IEEE Systems Journal
Volume	17
Issue number	4
DOIs	https://doi.org/10.1109/JSYST.2023.3302876
State	Published - 1 Dec 2023

Keywords

Deep learning
industrial cyber-physical system (ICPS)
low-rate denial-of-service attacks (LDoS)
metaheuristics

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10.1109/JSYST.2023.3302876

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title = "LDoS Attacks Detection for ICPS NB-IoTs Environment via SE-Based CNN",

abstract = "After Industry 4.0 was proposed, cyber-physical systems (CPS) were also introduced into the industrial environment called the industrial CPS. Internet-of-Things (IoT) services in the industry make intelligent decision-making more effective. In large enterprises, there are often requirements for cross-factory and transnational business. Narrowband IoT (NB-IoT) can provide wider coverage and indoor support, making deployment more flexible. However, NB-IoT is not equipped with powerful computing capabilities, preventing NB-IoT devices from having powerful information security software for defense. Thus, NB-IoT is vulnerable to low-rate denial-of-service attacks. Such attacks hide in normal traffic and are difficult to detect. This study uses a novel search economy to improve the weight combination search of a convolutional neural network to achieve a better detection rate.",

keywords = "Deep learning, industrial cyber-physical system (ICPS), low-rate denial-of-service attacks (LDoS), metaheuristics",

author = "Cho, {Hsin Hung} and Tsai, {Min Yan} and Zeng, {Jiang Yi} and Yu, {Chia Mu} and Chao, {Han Chieh} and Ilsun You",

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AU - Cho, Hsin Hung

AU - Tsai, Min Yan

AU - Zeng, Jiang Yi

AU - Yu, Chia Mu

AU - Chao, Han Chieh

AU - You, Ilsun

PY - 2023/12/1

Y1 - 2023/12/1

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KW - Deep learning

KW - industrial cyber-physical system (ICPS)

KW - low-rate denial-of-service attacks (LDoS)

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JF - IEEE Systems Journal

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LDoS Attacks Detection for ICPS NB-IoTs Environment via SE-Based CNN

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Keywords

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