Leveraging advanced technologies and strategies for port cyber resilience: Strengthening incident response and recovery

IF 3.8 Q2 TRANSPORTATION
Chalermpong Senarak
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引用次数: 0

Abstract

This study assesses incident response and recovery (IRR) tasks and their integration with smart technologies in port cybersecurity, with a focus on Laem Chabang Port (LCP), Thailand. Using the Delphi method, expert consensus was gathered to identify essential IRR tasks, challenges in their implementation, and smart technologies for improvement. The findings emphasize three key components for effective IRR: well-structured tasks, enhanced communication mechanisms, and the integration of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain. These technologies facilitate real-time monitoring, incident classification, and recovery coordination, thus enhancing both proactive and reactive cybersecurity measures. While the study focuses on LCP, its findings are applicable to ports globally, offering actionable insights for improving IRR frameworks, enhancing system integrity, and optimizing recovery efficiency. The study also highlights the need for transparent communication strategies and suggests the adoption of smart technologies to align with the global digital transformation of port operations. Future research should explore broader stakeholder involvement, comparative studies across ports, and empirical validation of smart technology effectiveness in real-world cybersecurity incidents.
利用先进技术和策略增强港口网络弹性:加强事件响应和恢复
本研究评估了事件响应和恢复(IRR)任务及其与港口网络安全智能技术的集成,重点是泰国林查邦港(LCP)。利用德尔菲法,专家们达成共识,以确定基本的IRR任务、实施中的挑战以及改进的智能技术。研究结果强调了有效IRR的三个关键组成部分:结构良好的任务、增强的沟通机制以及物联网(IoT)、人工智能(AI)和区块链等新兴技术的集成。这些技术促进了实时监控、事件分类和恢复协调,从而增强了主动和被动的网络安全措施。虽然该研究侧重于LCP,但其研究结果适用于全球港口,为改进IRR框架、增强系统完整性和优化恢复效率提供了可操作的见解。该研究还强调了透明沟通战略的必要性,并建议采用智能技术,以配合港口运营的全球数字化转型。未来的研究应探索更广泛的利益相关者参与,跨港口的比较研究,以及智能技术在现实世界网络安全事件中的有效性的实证验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Transportation Research Interdisciplinary Perspectives
Transportation Research Interdisciplinary Perspectives Engineering-Automotive Engineering
CiteScore
12.90
自引率
0.00%
发文量
185
审稿时长
22 weeks
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