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Sonification is the utilization of sounds to convey information about data or events. There are two types of emotions associated with sounds: (1) “perceived” emotions, in which listeners recognize the emotions expressed by the sound, and (2) “induced” emotions, in which listeners feel emotions induced by the sound. Although listeners may widely agree on the perceived emotion for a given sound, they often do not agree about the induced emotion of a given sound, so it is difficult to model induced emotions. This paper describes the development of several machine and deep learning models that predict the perceived and induced emotions associated with certain sounds, and it analyzes and compares the accuracy of those predictions. The results revealed that models built for predicting perceived emotions are more accurate than ones built for predicting induced emotions. However, the gap in predictive power between such models can be narrowed substantially through the optimization of the machine and deep learning models. This research has several applications in automated configurations of hardware devices and their integration with software components in the context of the Internet of Things, for which security is of utmost importance.

This paper reports a formative evaluation of auditory representations of cyber security threat indicators and cues, referred to as sonifications, to warn users about cyber threats. Most Internet browsers provide visual cues and textual warnings to help users identify when they are at risk. Although these alarming mechanisms are very effective in informing users, there are certain situations and circumstances where these alarming techniques are unsuccessful in drawing the user’s attention: (1) security warnings and features (e.g., blocking out malicious Websites) might overwhelm a typical Internet user and thus the users may overlook or ignore visual and textual warnings and, as a result, they might be targeted, (2) these visual cues are inaccessible to certain users such as those with visual impairments. This work is motivated by our previous work of the use of sonification of security warnings to users who are visually impaired. To investigate the usefulness of sonification in general security settings, this work uses real Websites instead of simulated Web applications with sighted participants. The study targets sonification for three different types of security threats: (1) phishing, (2) malware downloading, and (3) form filling. The results show that on average 58% of the participants were able to correctly remember what the sonification conveyed. Additionally, about 73% of the participants were able to correctly identify the threat that the sonification represented while performing tasks using real Websites. Furthermore, the paper introduces “CyberWarner”, a sonification sandbox that can be installed on the Google Chrome browser to enable auditory representations of certain security threats and cues that are designed based on several URL heuristics. Article highlights It is feasible to develop sonified cyber security threat indicators that users intuitively understand with minimal experience and training. Users are more cautious about malicious activities in general. However, when navigating real Websites, they are less informed. This might be due to the appearance of the navigating Websites or the overwhelming issues when performing tasks. Participants’ qualitative responses indicate that even when they did not remember what the sonification conveyed, the sonification was able to capture the user’s attention and take safe actions in response.

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