In today’s era of the Internet of Things, we are surrounded by security- and safety-critical, network-connected devices. In parallel with the rise in attacks on such devices, we have also seen an increase in devices that are abandoned, reached the end of their support periods, or will not otherwise receive future security updates. While this issue exists for a wide array of devices, those that use monolithic firmware, where the code and data are opaquely intermixed, have traditionally been difficult to examine and protect.

While online review services provide a two-way conversation between brands and consumers, malicious actors, including misbehaving businesses, have an equal opportunity to distort the reviews for their own gains. We propose OneReview, a method for locating fraudulent reviews, correlating data from multiple crowd-sourced review sites. Our approach utilizes Change Point Analysis to locate points at which a business' reputation shifts. Inconsistent trends in reviews of the same businesses across multiple websites are used to identify suspicious reviews. We then extract an extensive set of textual and contextual features from these suspicious reviews and employ supervised machine learning to detect fraudulent reviews.

In the past few years, both the industry and the academic communities have developed several approaches to detect malicious Android apps. State-of-the-art research approaches achieve very high accuracy when performing malware detection on existing datasets. These approaches perform their malware classification tasks in an "offline" scenario, where malware authors cannot learn from and adapt their malicious apps to these systems. In real-world deployments, however, adversaries get feedback about whether their app was detected, and can react accordingly by transforming their code until they are able to influence the classification.

Today's mobile applications increasingly rely on communication with a remote backend service to perform many critical functions, including handling user-specific information. This implies that some form of authentication should be used to associate a user with their actions and data. Since schemes involving tedious account creation procedures can represent "friction" for users, many applications are moving toward alternative solutions, some of which, while increasing usability, sacrifice security.

Outlier detection has been shown to be a promising machine learning technique for a diverse array of felds and problem areas. However, traditional, supervised outlier detection is not well suited for problems such as network intrusion detection, where proper labelled data is scarce. This has created a focus on extending these approaches to be unsupervised, removing the need for explicit labels, but at a cost of poorer performance compared to their supervised counterparts. Recent work has explored ways of making up for this, such as creating ensembles of diverse models, or even diverse learning algorithms, to jointly classify data. While using unsupervised, heterogeneous ensembles of learning algorithms has been proposed as a viable next step for research, the implications of how these ensembles are built and used has not been explored.