Grouping and Determining Perceived Severity of Cyber-Attack Consequences: Gaining Information Needed to Sonify Cyber-Attacks
Grouping and Determining Perceived Severity of Cyber-Attack Consequences: Gaining Information Needed to Sonify Cyber-Attacks
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DOI：
10.1007/s12193-022-00397-z
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发表时间：
2022-11-01
影响因子：
2.9
通讯作者：
Armstrong, Miriam
中科院分区：
文献类型：
--
作者：
Jones, Keith S.;Lodinger, Natalie R.;Widlus, Benjamin P.;Namin, Akbar Siami;Maw, Emily;Armstrong, Miriam
网络攻击是一个持续存在的问题。这些攻击对于视力受损的用户来说是有问题的，并且不能依赖视觉线索来指示潜在的网络攻击。声化是帮助视力受损用户检测潜在网络攻击的另一种方法。声音化使用非语音声音向用户提供信息。声音处理可以为视障用户提供有关其行为可能导致的潜在网络攻击后果的信息。然而，要让网络攻击的后果听起来清晰，有两个挑战。首先，有许多潜在的网络攻击后果需要声音化，而人类记忆声音化及其含义之间联系的能力有限。其次，当网络攻击警告消息将后果的严重性与用户感知的严重性相一致时，它们会更受信任。然而，我们不知道个人后果的严重性。因此，我们需要减少要用声音表达的后果的数量，并确定这些后果的感知严重性。我们让非专家参与者根据感知的相似性对网络攻击后果进行分组。分析显示，参与者的分组形成了七个集群。然后，我们让非专家参与者对每个网络攻击后果的严重程度进行评估。这些评级被用来确定每个集群的感知严重性。这些努力产生了一组网络攻击后果集群，这些集群（a）足够小，用户应该能够记住发音及其含义之间的关联，并且（B）可以以反映用户对集群网络攻击后果严重性的感知的方式进行发音。因此，这些研究的结果是创建有效的声音作为网络安全警告信息的关键步骤。
Cyber-attacks are a continuing problem. These attacks are problematic for users who are visually impaired and cannot rely on visual cues to indicate a potential cyber-attack. Sonification is an alternative way to help users who are visually impaired detect potential cyber-attacks. Sonification provides information to users using non-speech sounds. Sonification could provide users who are visually impaired with information on potential cyber-attack consequences that could stem from their actions. However, there are two challenges with sonifying cyber-attack consequences. First, there are many potential cyber-attack consequences to sonify, and humans have a limited ability to remember associations between sonifications and their meanings. Second, cyber-attack warning messages are better trusted when they align the severity of the consequences with the user’s perceived severity. However, we do not know the perceived severity of individual consequences. Therefore, we need to reduce the number of consequences to sonify and to determine the perceived severity of these consequences. We had non-expert participants group cyber-attack consequences based on perceived similarity. Analyses revealed that participants’ groupings formed seven clusters. We then had non-expert participants rate the perceived severity of each cyber-attack consequence. Those ratings were used to determine the perceived severity of each cluster. These efforts resulted in a set of cyber-attack consequence clusters that (a) is small enough that users should be able to remember associations between sonifications and their meanings, and (b) can be sonified in a way that reflects users’ perceptions regarding the severity of the clustered cyber-attack consequences. As such, the results of these studies are critical steps towards creating effective sonifications that serve as cyber-security warning messages.
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