信息安全是导致船舶航运安全风险发生的主要原因，为保障船舶航运安全，设计基于数据挖掘的船舶信息安全风险检测方法。采用网络爬虫技术收集船舶基本信息、网络通信信息、技术信息等，并对信息实施清洗、集成与转换等预处理。采用关联分析方法提取船舶信息安全风险特征，考虑船舶信息全生命周期的时序性特征，对提取时船舶信息安全特征进行实时变换处理。利用数据挖掘中的支持向量机模型构建舰船信息安全风险检测模型，利用沙丘猫群算法优化模型参数，仿真实验结果表明，该方法能够有效获取研究对象信息安全风险检测结果，降低各类信息安全风险事件发生的概率70%以上，由此说明该方法能够有效保障船舶航运安全。
Information security is the main cause of ship shipping safety risks. To ensure ship shipping safety, a ship information security risk detection method based on data mining is designed. Using web crawler technology to collect basic ship information, network communication information, technical information, etc., and implementing preprocessing such as cleaning, integration, and transformation of the information. Using correlation analysis method to extract ship information security risk features, considering the temporal characteristics of the entire lifecycle of ship information, real-time transformation processing is carried out on the extracted ship information security features. Using support vector machine models in data mining to construct a ship information security risk detection model, optimizing model parameters using the Dune Cat Swarm Algorithm. Simulation experiments show that this method can effectively obtain the information security risk detection results of the research object, reduce the probability of various information security risk events by more than 70%, indicating that this method can effectively ensure the safety of ship navigation.
2024,46(13): 171-174 收稿日期：2024-01-18
DOI：10.3404/j.issn.1672-7649.2024.13.031
分类号：TP391
作者简介：张懿爵(1986-),男,硕士,高级工程师/讲师,研究方向为计算机科学与技术及人工智能
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