Evaluating the reliability of users as human sensors of social media security threats

Ryan Heartfield, G. Loukas
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引用次数: 8

Abstract

While the human as a sensor concept has been utilised extensively for the detection of threats to safety and security in physical space, especially in emergency response and crime reporting, the concept is largely unexplored in the area of cyber security. Here, we evaluate the potential of utilising users as human sensors for the detection of cyber threats, specifically on social media. For this, we have conducted an online test and accompanying questionnaire-based survey, which was taken by 4,457 users. The test included eight realistic social media scenarios (four attack and four non-attack) in the form of screenshots, which the participants were asked to categorise as “likely attack” or “likely not attack”. We present the overall performance of human sensors in our experiment for each exhibit, and also apply logistic regression to evaluate the feasibility of predicting that performance based on different characteristics of the participants. Such prediction would be useful where accuracy of human sensors in detecting and reporting social media security threats is important. We identify features that are good predictors of a human sensor's performance and evaluate them in both a theoretical ideal case and two more realistic cases, the latter corresponding to limited access to a user's characteristics.
评估用户作为社交媒体安全威胁的人类传感器的可靠性
虽然人类作为传感器的概念已被广泛用于探测物理空间的安全和安保威胁,特别是在应急反应和犯罪报告中,但这一概念在网络安全领域基本上尚未得到探索。在这里,我们评估了利用用户作为人类传感器来检测网络威胁的潜力,特别是在社交媒体上。为此,我们进行了一项在线测试和附带的问卷调查,共有4457名用户参与。测试包括8个真实的社交媒体场景(4个攻击和4个非攻击),以截图的形式呈现,参与者被要求将其归类为“可能攻击”或“可能不攻击”。我们在每个展览中展示了人体传感器的整体性能,并应用逻辑回归来评估基于参与者不同特征预测性能的可行性。在人类传感器检测和报告社交媒体安全威胁的准确性很重要的情况下,这种预测将是有用的。我们确定了能够很好地预测人体传感器性能的特征,并在理论上的理想情况下和两个更现实的情况下对它们进行了评估,后者对应于对用户特征的有限访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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