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Home networks lack the powerful security tools and trained personnel available in enterprise networks. This compli- cates efforts to address security risks in residential settings. While prior efforts explore outsourcing network traffic to cloud or cloudlet services, such an approach exposes that network traffic to a third party, which introduces privacy risks, particularly where traffic is decrypted (e.g., using Transport Layer Security Inspection (TLSI)). To enable security screening locally, home networks could introduce new physical hardware, but the capital and deployment costs may impede deployment. In this work, we explore a system to leverage existing available devices, such as smartphones, tablets and laptops, already inside a home network to create a platform for traffic inspection. This software-based solution avoids new hardware deployment and allows decryption of traffic without risk of new third parties. Our investigation compares on-router inspection of traffic with an approach using that same router to direct traffic through smartphones in the local network. Our performance evaluation shows that smartphone middleboxes can substantially increase the throughput of communication from around 10 Mbps in the on-router case to around 90 Mbps when smartphones are used. This approach increases CPU usage at the router by around 15%, with a 20% CPU usage increase on a smartphone (with single core processing). The network packet latency increases by about 120 milliseconds. 

Given the complexity of modern systems, it can be difficult for device defenders to pinpoint the user action that precipitates a network connection. Mobile devices, such as smartphones, further complicate analysis since they may have diverse and ephemeral network connectivity and support users in both personal and professional capacities. There are multiple stakeholders associated with mobile devices, such as the end-user, device owner, and each organization whose assets are accessed via the device; however, none may be able to fully manage, troubleshoot, or defend the device on their own. In this work, we explore a set of techniques to determine the root cause of each new network flow, such the button press or gesture for user-initiated flows, associated with a mobile device. We fuse the User Interface (UI) context with network flow data to enhance network profiling on the Android operating system. In doing so, we find that we can improve network profiling by clearly linking user actions with network behavior. When exploring effectiveness, the system enables allow-lists to reach over 99% accuracy, even when user-specified destinations are used. 

The security of Internet-of-Things (IoT) devices in the residential environment is important due to their widespread presence in homes and their sensing and actuation capabilities. However, securing IoT devices is challenging due to their varied designs, deployment longevity, multiple manufacturers, and potentially limited availability of long-term firmware updates. Attackers have exploited this complexity by specifically targeting IoT devices, with some recent high-profile cases affecting millions of devices. In this work, we explore access control mechanisms that tightly constrain access to devices at the residential router, with the goal of precluding access that is inconsistent with legitimate users' goals. Since many residential IoT devices are controlled via applications on smartphones, we combine application sensors on phones with sensors at residential routers to analyze workflows. We construct stateful filters at residential routers that can require user actions within a registered smartphone to enable network access to an IoT device. In doing so, we constrain network packets only to those that are consistent with the user's actions. In our experiments, we successfully identified 100% of malicious traffic while correctly allowing more than 98% of legitimate network traffic. The approach works across device types and manufacturers with straightforward API and state machine construction for each new device workflow. 

Crustal thickness, elevation, and Sr/Y and (La/Yb)_Nof magmatic rocks are strongly correlated for subduction‐related and collision‐related mountain belts. We quantitatively constrain the paleo‐elevation of the Tibetan Plateau since the Cretaceous using empirically derived equations. The results are broadly consistent with previous estimates based on stable isotope and structural analyses, supporting a complex uplift history. Our data suggest that a protoplateau formed in central Tibet during the Late Cretaceous and was higher than the contemporaneous Gangdese arc. This protoplateau collapsed before the India‐Asia collision, during the same time period that elevation in southern Tibet was increasing. During the India‐Asia collision, northern and southern Tibet were uplifted first followed by renewed uplift in central Tibet, which suggests a more complicated uplift history than commonly believed. We contend that a broad paleovalley formed during the Paleogene in central Tibet and that the whole Tibetan Plateau reached present‐day elevations during the Miocene.