Unifying deep learning and knowledge representation for cybersecurity applications.

统一网络安全应用的深度学习和知识表示。

• 批准号: 133167
• 金额: $ 8.9万
• 依托单位: ANON AI LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=133167
• 关键词: Unifying; deep; learning; knowledge; representation

Companies are increasingly generating more data. There’s increasing value in sharing and analysing this data. However, it often contains personal and sensitive information and it’s very important to protect customer privacy by not sharing this information with third parties. This leads to a catch 22 situation. Companies have data they want to share but they can’t because of what’s in the data. Automated tools can be used to analyse the data and redact the sensitive information. However, they can’t currently be relied on because they’re not accurate enough. This is because accurate classification often depends on understanding context, which is something that computer systems struggle with. This project aims to design a new type of automated classification system that fuses best in class topological, contextual data analysis and natural language processing algorithms. This system will be able to understand both the structural and contextual information that existing automated systems can’t handle. This will allow personal and sensitive information to be identified much more accurately, which in turn will allow companies to share and extract value from their data.

公司越来越多地产生更多的数据。分享和分析这些数据的价值越来越大。然而，它通常包含个人和敏感信息，通过不与第三方共享这些信息来保护客户隐私非常重要。这导致了第22条军规的出现。公司有他们想要分享的数据，但由于数据中的内容，他们不能分享。自动化工具可用于分析数据和编辑敏感信息。然而，它们目前不能被依赖，因为它们不够准确。这是因为准确的分类通常依赖于对上下文的理解，而这是计算机系统难以处理的问题。该项目旨在设计一种新型的自动分类系统，该系统融合了类内最好的拓扑、上下文数据分析和自然语言处理算法。这个系统将能够理解现有自动化系统无法处理的结构和上下文信息。这将使个人和敏感信息被更准确地识别，这反过来将允许公司共享并从其数据中提取价值。