在入侵检测系统中整合可解释的人工智能的系统性审查,以提高网络安全的透明度和可解释性
Vincent Zibi Mohale1, Ibidun Christiana Obagbuwa1
1Faculty of Natural and Applied Sciences, Department of Computer Science and Information Technology, Sol Plaatje University, Kimberley, South Africa.
Frontiers in artificial intelligence
|March 5, 2025
概括
可解释的人工智能 (XAI) 通过提高透明度来增强入侵检测系统 (IDS). 本次审查强调了基于规则和基于树的XAI模型是可解释的,解决了网络安全中的黑子问题.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 可解释的人工智能
背景情况:
- 复杂的网络威胁需要先进的入侵检测系统 (IDS).
- 传统的IDS经常使用不透明的机器学习模型,为分析师创造了一个"黑子"的挑战.
- 可解释的人工智能 (XAI) 为IDS提供了透明度和可解释性.
研究的目的:
- 系统地审查XAI在IDS中的整合.
- 通过XAI提高网络安全的透明度和可解释性.
- 确定IDS的有效XAI技术及其局限性.
主要方法:
- 对IDS中XAI的最新研究进行了全面分析.
- 在IDS框架内评估常用的XAI技术.
- 检查XAI对IDS的好处和局限性.
主要成果:
- 基于规则和基于树的XAI模型在IDS中被青为可解释性.
- 在XAI解释性和IDS检测准确性之间的权衡仍然存在.
- 在IDS中XAI的标准化和可扩展性方面存在差距.
结论:
- 在IDS中XAI的整合对于透明度和信任至关重要.
- 未来的IDS需要混合模型和实时可解释性.
- 未来的研究应该专注于为IDS量身定制的XAI技术,标准化指标和伦理框架.
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