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

Research output: Contribution to journalArticlepeer-review

1 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 languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Systems Journal
DOIs
StateAccepted/In press - 2023

Keywords

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

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