在脆弱性风险评估中使用威胁情报时的算法感知
Sarah van Gerwen1, Aurora Papotti1, Katja Tuma2
1Informatica, Vrije Universiteit, Amsterdam, The Netherlands.
概括
参与者不信任人工智能驱动的网络威胁情报建议. 安全专业知识增加了信任,但对于人类与人工智能 (AI) 来源的感知偏见仍然相似.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 政府和商业部门越来越多地采用人工智能进行网络威胁情报分析.
- 在生产部署自动化解决方案之前,了解人类与人工智能来源的潜在偏见至关重要.
研究的目的:
- 通过网络威胁情报 (人与人工智能) 的来源来衡量引入的偏见.
- 评估参与者专业知识 (安全和机器学习) 对偏见的影响.
主要方法:
- 对57名士生进行了受控实验.
- 参与者分析了利用操纵来源 (人类专家或AI算法) 的网络威胁情报报告.
- 测量了被认为的偏见和对建议的同意.
主要成果:
- 参与者倾向于不同意人工智能产生的建议.
- 更高的安全专业知识与更大程度上同意建议相关.
- 感知偏差在统计学上是相当的,无论建议来自人类还是人工智能来源.
- 不同意建议,无论来源如何,是影响感知偏见的主要因素.
结论:
- 在网络威胁情报中引入人工智能可能会影响一级SOC分析师.
- 需要与经验丰富的安全专业人员进行进一步的研究,以将研究结果推广到专业实践中.
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