Serverless computing is supplanting past versions of cloud computing as the easiest way to rapidly prototype and deploy applications. However, the reentrant and ephemeral nature of serverless functions only exacerbates the challenge of correctly specifying security policies. Unfortunately, with role-based access control solutions like Amazon Identity and Access Management (IAM) already suffering from pervasive misconfiguration problems, the likelihood of policy failures in serverless applications is high.

Historically, enterprise network reconnaissance is an active process, often involving port scanning. However, as routers and switches become more complex, they also become more susceptible to compromise. From this vantage point, an attacker can passively identify high-value hosts such as the workstations of IT administrators, C-suite executives, and finance personnel. The goal of this paper is to develop a technique to deceive and dissuade such adversaries. We propose HoneyRoles, which uses honey connections to build metaphorical haystacks around the network traffic of client hosts belonging to high-value organizational roles. The honey connections also act as network canaries to signal network compromise, thereby dissuading the adversary from acting on information observed in network flows. We design a prototype implementation of HoneyRoles an OpenFlow SDN controller and evaluate its security using the PRISM probabilistic model checker. Our performance evaluation shows that HoneyRoles has a small effect on network request completion time, and security analysis demonstrates that once an alert is raised, HoneyRoles can quickly identify the compromised switch with high probability. In doing so, we show that role-based network deception is a promising approach for defending against adversaries in compromised network devices.

Third-party security analytics allow companies to outsource threat monitoring tasks to teams of experts and avoid the costs of in-house security operations centers. By analyzing telemetry data from many clients these services are able to offer enhanced insights, identifying global trends and spotting threats before they reach most customers. Unfortunately, the aggregation that drives these insights simultaneously risks exposing sensitive client data if it is not properly sanitized and tracked. In this work, we present SCIFFS, an automated information flow monitoring framework for preventing sensitive data exposure in third-party security analytics platforms. SCIFFS performs decentralized information flow control over customer data it in a serverless setting, leveraging the innate polyinstantiated nature of serverless functions to assure precise and lightweight tracking of data flows. Evaluating SCIFFS against a proof-of-concept security analytics framework on the widely-used OpenFaaS platform, we demonstrate that our solution supports common analyst workflows data ingestion, custom dashboards, threat hunting) while imposing just 3.87% runtime overhead on event ingestion and the overhead on aggregation queries grows linearly with the number of records in the database (e.g., 18.75% for 50,000 records and 104.27% for 500,000 records) as compared to an insecure baseline. Thus, SCIFFS not only establishes a privacy-respecting model for third-party security analytics, but also highlights the opportunities for security-sensitive applications in the serverless computing model.

Web servers are a popular target for adversaries as they are publicly accessible and often vulnerable to compromise. Compromises can go unnoticed for months, if not years, and recovery often involves a complete system rebuild. In this paper, we propose n-m-Variant Systems, an adversarial-resistant software rejuvenation framework for cloud-based web applications. We improve the state-of-the-art by introducing a variable m that provides a knob for administrators to tune an environment to balance resource usage, performance overhead, and security guarantees. Using m, security guarantees can be tuned for seconds, minutes, days, or complete resistance. We design and implement an n-m-Variant System prototype to protect a Mediawiki PHP application serving dynamic content from an external SQL persistent storage. Our performance evaluation shows a throughput reduction of 65% for 108 seconds of resistance and 83% for 12 days of resistance to sophisticated adversaries, given appropriate resource allocation. Furthermore, we use theoretical analysis and simulation to characterize the impact of system parameters on resilience to adversaries. Through these efforts, our work demonstrates how properties of cloud-based servers can enhance the integrity of Web servers.