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AbstractThis 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 highlightsIt 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. 

With the continued improvement and innovation, technology has become an integral part of our daily lives. The rapid adoption of technology and its affordability has given rise to the Internet-of-Things (IoT). IoT is an interconnected network of devices that are able to communicate and share information seamlessly. IoT encompasses a gamut of heterogeneous devices ranging from a small sensor to large industrial machines. One such domain of IoT that has seen a significant growth in the recent few years is that of the wearable devices. While the privacy issues for medical devices has been well-researched and documented in the literature, the threats to privacy arising from the use of consumer wearable devices have received very little attention from the research community. This paper presents a survey of the literature to understand the various privacy challenges, mitigation strategies, and future research directions as a result of the widespread adoption of wearable devices. 

Billions of devices in the Internet of Things (IoT) are inter-connected over the internet and communicate with each other or end users. IoT devices communicate through messaging bots. These bots are important in IoT systems to automate and better manage the work flows. IoT devices are usually spread across many applications and are able to capture or generate substantial influx of big data. The integration of IoT with cloud computing to handle and manage big data, requires considerable security measures in order to prevent cyber attackers from adversarial use of such large amount of data. An attacker can simply utilize the messaging bots to perform malicious activities on a number of devices and thus bots pose serious cybersecurity hazards for IoT devices. Hence, it is important to detect the presence of malicious bots in the network. In this paper we propose an evidence theory-based approach for malicious bot detection. Evidence Theory, a.k.a. Dempster Shafer Theory (DST) is a probabilistic reasoning tool and has the unique ability to handle uncertainty, i.e. in the absence of evidence. It can be applied efficiently to identify a bot, especially when the bots have dynamic or polymorphic behavior. The key characteristic of DST is that the detection system may not need any prior information about the malicious signatures and profiles. In this work, we propose to analyze the network flow characteristics to extract key evidence for bot traces. We then quantify these pieces of evidence using apriori algorithm and apply DST to detect the presence of the bots.