<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>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.</p>
</abstract>

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.

Blockchain-Enabled Transfer Learning for Vulnerability Detection and Mitigation in Maritime Logistics

Department of Computer Science and Engineering

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for...

Blockchain-Enabled Transfer Learning for Vulnerability Detection and Mitigation in Maritime Logistics

Pubblicato online: 29 mar 2024

Pagine: 135 - 145

DOI: https://doi.org/10.2478/pomr-2024-0014

Parole chiave
Logistic management, Blockchain, Transfer Learning, Marine ecosystem, Vulnerability detection

© 2024 J Chandra Priya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Blockchain-Enabled Transfer Learning for Vulnerability Detection and Mitigation in Maritime Logistics

Pubblicato online: 29 mar 2024

Pagine: 135 - 145

DOI: https://doi.org/10.2478/pomr-2024-0014

Parole chiaveLogistic management, Blockchain, Transfer Learning, Marine ecosystem, Vulnerability detection

© 2024 J Chandra Priya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Logistic management, Blockchain, Transfer Learning, Marine ecosystem, Vulnerability detection