A Neural Network Based Cyber Insurance Risk Classification Tool

基于神经网络的网络保险风险分类工具

• 批准号: 514823-2017
• 负责人: Ghorbani, Aliakbar
• 金额: $ 1.82万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639306
• 关键词: Neural; Network; Based; Cyber; Insurance

Cyber insurance underwriters need to rapidly assess the cybersecurity risk of prospective cyber insuranceclients in order to quote appropriate premiums for different cyber insurance coverages. Underwriters arecurrently relying heavily on their expert professional judgment to determine the key indicators of cybersecurityrisks for different coverages. The Primary objective of this research project is to design, develop, and test aneural network-based cyber risk classification tool that can adapt to the changing indicators of cyber risk andmake cyber incident risks classification based on current and historical trends in malicious cyber activities.

网络保险承保人需要快速评估潜在网络保险客户的网络安全风险，以便为不同的网络保险范围报价合适的保费。目前，保险商在确定不同承保范围的网络安全风险关键指标时，严重依赖于他们的专家专业判断。本研究项目的主要目标是设计、开发和测试基于神经网络的网络风险分类工具，该工具能够适应不断变化的网络风险指标，并根据恶意网络活动的当前和历史趋势进行网络事件风险分类。