Improved Security Requirements Engineering using Knowledge Representation
使用知識表示的改進的安全需求工程 摘要 在本文中，我們爲SysML-Sec框架引入了一個安全元模型，該模型是通過使用知識表示技術來顯式表示安全問題而開發的，旨在改善安全需求工程流程。這種元模型可以指定本體概念，這些本體概念定義了通過SysML-Sec圖引入的安全工件的語義。該元模型還可以表示將幾個這樣的概念聯繫在一起的關係。然後，使用此表示法來推理系統設計師和安全專家通過SysML-Secfram
相關文章
- 1. Towards the Model-Driven Engineering of Security Requirements for Embedded System
- 2. arxiv_FDNet_Face Detection Using Improved Faster RCNN
- 3. Part 1 – Reverse engineering using Androguard
- 4. Foundations of Software Engineering 和 Software Engineering Concepts
- 5. Improved Neural Relation Detection for Knowledge Base Question Answering
- 6. 【EMNLP2019】Event Representation Learning Enhanced with External Commonsense Knowledge
- 7. Something about Software Engineering
- 8. Software Engineering: 3. Project planning
- 9. arxiv2017_Face Detection using Deep Learning：An Improved Faster RCNN Approach
- 10. Improved Object Categorization and Detection Using Comparative Object Similarity
- 更多相關文章...
- • W3C XQuery 活動 - W3C 教程
- • SQLite 別名 - SQLite教程
- • RxJava操作符（六）Utility
- • C# 中 foreach 遍歷的用法
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Towards the Model-Driven Engineering of Security Requirements for Embedded System
- 2. arxiv_FDNet_Face Detection Using Improved Faster RCNN
- 3. Part 1 – Reverse engineering using Androguard
- 4. Foundations of Software Engineering 和 Software Engineering Concepts
- 5. Improved Neural Relation Detection for Knowledge Base Question Answering
- 6. 【EMNLP2019】Event Representation Learning Enhanced with External Commonsense Knowledge
- 7. Something about Software Engineering
- 8. Software Engineering: 3. Project planning
- 9. arxiv2017_Face Detection using Deep Learning：An Improved Faster RCNN Approach
- 10. Improved Object Categorization and Detection Using Comparative Object Similarity