保护数字资产使用基于本体学的网络情境意识系统
Tariq Ammar Almoabady1, Yasser Mohammad Alblawi1, Ahmad Emad Albalawi1
1Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.
Frontiers in artificial intelligence
|January 24, 2025
概括
这项研究通过使用隔离森林和自动编码算法来提高网络局势意识,以积极检测威胁. 综合方法显著提高了网络安全威胁情报和检测能力.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 实时检测和缓解网络安全威胁至关重要.
- 现有的方法往往缺乏全面的威胁情报和积极的异常检测能力.
研究的目的:
- 引入一个综合方法,集成隔离森林和自编码算法,STIX和本体论开发.
- 通过一种新的双算法框架来增强网络安全威胁检测和情报.
主要方法:
- 利用隔离森林用于异常识别和自动编码器用于特征学习和非线性检测.
- 使用结构化威胁信息表达 (STIX) 框架进行标准化威胁信息.
- 用于系统知识表示和语义威胁相关联的本体论开发.
主要成果:
- 实现了95%的准确性,99%的F1得分和94.60%的回忆率.
- 与现有基准相比,在异常检测方面表现优越.
- 强调了该模型在提高网络情境意识方面的有效性.
结论:
- 综合方法大大改善了威胁信息标准化和语义分析.
- 双算法方法提供了可扩展和有效的网络安全应用.
- 未来的工作应该集中在现实世界的部署和进一步优化.
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