Blockchain-Enabled Transfer Learning for vulnerability detection and mitigation in maritime Logistics

• Autorzy: J, Chandra Priya , Rudzki, Krzysztof , Nguyen, Xuan Huong , Nguyen, Hoang Phuong , Chotechuang, Naruphun , Pham, Nguyen Dang Khoa
• Języki publikacji: EN

Abstrakty

EN

With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.

Słowa kluczowe

EN

logistic management; blockchain; Transfer Learning; marine ecosystem; vulnerability detection

• Czasopismo: Polish Maritime Research
• Rocznik: 2024
• Tom: nr 1
• Strony: 135--145
• Opis fizyczny: Bibliogr. 72 poz., rys., tab.

Twórcy

autor

J, Chandra Priya

• Department of Computer Science and Engineering, Mepco Schenk Engineering College, Sivakasi, India

autor

Rudzki, Krzysztof

• Faculty of Marine Engineering, Gdynia Maritime University, Gdansk, Poland

autor

Nguyen, Xuan Huong

• Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam

autor

Nguyen, Hoang Phuong

• Academy of Politics Region II, Ho Chi Minh City, Viet Nam, Viet Nam

autor

Chotechuang, Naruphun

• Faculty of International Maritime Studies, Kasetsart University, Sri Racha Campus, Chonburi, Thailand

autor

Pham, Nguyen Dang Khoa

• PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam,

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Uwagi

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Identyfikatory

DOI

10.2478/pomr-2024-0014