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To achieve economies of scale, popular Internet destinations concurrently serve hundreds or thousands of users on shared physical infrastructure. This resource sharing enables attacks that misuse permissions and affect other users. Our work uses containerization to create "single-use servers" which are dynamically instantiated and tailored for each user's permissions. This isolates users and eliminates attacker persistence. Further, it simplifies analysis, allowing the fusion of logs to help defenders localize vulnerabilities associated with security incidents. We thus mitigate attacks and convert them into debugging traces to aid remediation. We evaluate the approach using three systems, including the popular WordPress content management system. It eliminates attacker persistence, propagation, and permission misuse. It has low CPU and latency costs and requires linear memory consumption, which we reduce with a customized page merging technique. 

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. 

Phone-based authenticators (PBAs) are commonly incorporated into multi-factor authentication and passwordless login schemes for corporate networks and systems. These systems require users to prove that they possess a phone or phone number associated with an account. The out-of-band nature of PBAs and their security may not be well understood by users. Further, the frequency of PBA prompts may desensitize users and lead to increased susceptibility to phishing or social engineering. We explore such risks to PBAs by exploring PBA implementation options and two types of attacks. When employed with a real-world PBA system, we found the symptoms of such attacks were subtle. A subsequent user study revealed that none of our participants noticed the attack symptoms, highlighting the limitations and risks associated with PBAs. 

Interactive web-based applications play an important role for both service providers and consumers. However, web applications tend to be complex, produce high-volume data, and are often ripe for attack. Attack analysis and remediation are complicated by adversary obfuscation and the difficulty in assembling and analyzing logs. In this work, we explore the web application analysis task through log file fusion, distillation, and visualization. Our approach consists of visualizing the logs of web and database traffic with detailed function execution traces. We establish causal links between events and their associated behaviors. We evaluate the effectiveness of this process using data volume reduction statistics, user interaction models, and usage scenarios. Across a set of scenarios, we find that our techniques can filter at least 97.5% of log data and reduce analysis time by 93-96%. 

The software-defined networking (SDN) paradigm offers significant flexibility for network operators. However, the SDN community has focused on switch-based implementations, which pose several challenges. First, some may require significant hardware costs to upgrade a network. Further, fine-grained flow control in a switch-based SDN results in well-known, fundamental scalability limitations. These challenges may limit the reach of SDN technologies. In this work, we explore the extent to which host-based SDN agents can achieve feature parity with switch-based SDNs. Prior work has shown the potential of host-based SDNs for security and access control. Our study finds that with appropriate preparation, a host-based agent offers the same capabilities of switch-based SDNs in the remaining key area of traffic engineering, even in a legacy managed-switch network. We find the approach offers comparable performance to switch-based SDNs while eliminating the flow table scalability and cost concerns of switch-based SDN deployments. 

Endpoint sensors play an important role in an organization's network defense. However, endpoint sensors may be disabled or sabotaged if an adversary gains root-level access to the endpoint running the sensor. While traditional sensors cannot reliably defend against such compromises, this work explores an approach to detect these compromises in applications where multiple sensors can be correlated. We focus on the OpenFlow protocol and show that endpoint sensor data can be corroborated using a remote endpoint's sensor data or that of in-network sensors, like an OpenFlow switch. The approach allows end-to-end round trips of less than 20ms for around 90% of flows, which includes all flow elevation and processing overheads. In addition, the approach can detect flows from compromised nodes if there is a single uncompromised sensor on the network path. This approach allows defenders to quickly identify and quarantine nodes with compromised endpoint sensors.