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The number of IoT devices in smart homes is increasing. This broad adoption facilitates users' lives, but it also brings problems. One such issue is that some IoT devices may invade users' privacy. Some reasons for this invasion can stem from obscure data collection practices or hidden devices. Specific IoT devices can exist out of sight and still collect user data to send to third parties via the Internet. Owners can easily forget the location or even the existence of these devices, especially if the owner is a landlord who manages several properties. The landlord-owner scenario creates multi-user problems as designers build machines for single users. We developed tags that use wireless protocols, buzzers, and LED lighting to lead users to solve the issue of device discovery in shared spaces and accommodate multi-user scenarios. They are attached to IoT devices inside a unit during their installation to be later discovered by a tenant. These tags have similar functionalities as the popular Tile models or Airtag, but our tags have different features based on our privacy use case. Our tags do not require pairing; multiple users can interact with them through our Android application. Although researchers developed several other tools, such as thermal cameras or virtual reality (VR), for discovering devices in environments, they have not used wireless protocols as a solution. We measured specific performance metrics of our tags to analyze their feasibility for this problem. We also conducted a user study to measure the participants' comfort levels while finding objects with our tags attached. Our results indicate that wireless tags can be viable for device tracking in residential properties.

With the rapid development of machine learning for image classification, researchers have found new applications of visualization techniques in malware detection. By converting binary code into images, researchers have shown satisfactory results in applying machine learning to extract features that are difficult to discover manually. Such visualization-based malware detection methods can capture malware patterns from many different malware families and improve malware detection speed. On the other hand, recent research has also shown adversarial attacks against such visualization-based malware detection. Attackers can generate adversarial examples by perturbing the malware binary in non-reachable regions, such as padding at the end of the binary. Alternatively, attackers can perturb the malware image embedding and then verify the executability of the malware post-transformation. One major limitation of the first attack scenario is that a simple pre-processing step can remove the perturbations before classification. For the second attack scenario, it is hard to maintain the original malware's executability and functionality. In this work, we provide literature review on existing malware visualization techniques and attacks against them. We summarize the limitation of the previous work, and design a new adversarial example attack against visualization-based malware detection that can evade pre-processing filtering and maintain the original malware functionality. We test our attack on a public malware dataset and achieve a 98% success rate.

Software engineers regularly use JavaScript and Python for both front-end and back-end automation tasks. On top of JavaScript and Python, there are several frameworks to facilitate automation tasks further. Some of these frameworks are Node Manager Package (npm) and Python Package Index (PyPi), which are open source (OS) package libraries. The public registries npm and PyPi use to host packages allow any user with a verified email to publish code. The lack of a comprehensive scanning tool when publishing to the registry creates security concerns. Users can report malicious code on the registry; however, attackers can still cause damage until they remove their tool from the platform. Furthermore, several packages depend on each other, making them more vulnerable to a bad package in the dependency tree. The heavy code reuse creates security artifacts developers have to consider, such as the package reach. This project will illustrate a high-level overview of common risks associated with OS registries and the package dependency structure. There are several attack types, such as typosquatting and combosquatting, in the OS package registries. Outdated packages pose a security risk, and we will examine the extent of technical lag present in the npm environment. In this paper, our main contribution consists of a survey of common threats in OS registries. Afterward, we will offer countermeasures to mitigate the risks presented. These remedies will heavily focus on the applications of Machine Learning (ML) to detect suspicious activities. To the best of our knowledge, the ML-focused countermeasures are the first proposed possible solutions to the security problems listed. In addition, this project is the first survey of threats in npm and PyPi, although several studies focus on a subset of threats.