Digital Contact-tracing through mobile applications require gathering of location and other personal information of an individual by the government or private organizations and became an essential solution for moderating the pandemic and slackening lockdown measures. However, the moral and legal boundaries for such privacy-sensitive information reconnaissance procedure and the ambiguity in the security measures of such technologies has gained controversial reputation.In this work, we performed static profiling of 10 different Android Contact-tracing applications, developed by the health departments of 10 different states within the United States and studied possible security threats posed by them. To the best of our knowledge, our work is the first to heuristically analyze the users’ attitude towards these applications to understand the user-perceived contribution of these apps towards their well-being. We collected user feedback for each of the apps and trained a logistic regression classifier on cleaned, pre-processed and vectorized texts to identify positive or negative outlook towards these apps. Using the confusion matrix, our predictive model showed up to 85% accuracy, 94% precision, 93% recall and 83% f1 score. in predicting the sentiments. The sentiment prediction shows, users in some states did find the apps to be helpful where some other states found them wasteful. Whereas, our static analysis shows none of the apps are malicious themselves but all of them request permission that can be abused to gain escalated privileges.

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.

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.