Detecting and explaining the nature of attacks in distributed web services is often difficult -- determining the nature of suspicious activity requires following the trail of an attacker through a chain of heterogeneous software components including load balancers, proxies, worker nodes, and storage services. Unfortunately, existing forensic solutions cannot provide the necessary context to link events across complex workflows, particularly in instances where application layer semantics (e.g., SQL queries, RPCs) are needed to understand the attack. In this work, we present a transparent provenance-based approach for auditing web services through the introduction of Network Provenance Functions (NPFs). NPFs are a distributed architecture for capturing detailed data provenance for web service components, leveraging the key insight that mediation of an application's protocols can be used to infer its activities without requiring invasive instrumentation or developer cooperation. We design and implement NPF with consideration for the complexity of modern cloud-based web services, and evaluate our architecture against a variety of applications including DVDStore, RUBiS, and WikiBench to show that our system imposes as little as 9.3% average end-to-end overhead on connections for realistic workloads. Finally, we consider several scenarios in which our system can be used to concisely explain attacks. NPF thus enables the hassle-free deployment of semantically rich provenance-based auditing for complex applications workflows in the Cloud.

Modern organizations face increasingly complex information management requirements. A combination of commercial needs, legal liability and regulatory imperatives has created a patchwork of mandated policies. Among these, personally identifying customer records must be carefully access-controlled, sensitive files must be encrypted on mobile computers to guard against physical theft, and intellectual property must be protected from both exposure and "poisoning." However, enforcing such policies can be quite difficult in practice since users routinely share data over networks and derive new files from these inputs--incidentally laundering any policy restrictions. In this paper, we describe a virtual machine monitor system called Neon that transparently labels derived data using byte-level "tints" and tracks these labels end to end across commodity applications, operating systems and networks. Our goal with Neon is to explore the viability and utility of transparent information flow tracking within conventional networked systems when used in the manner in which they were intended. We demonstrate that this mechanism allows the enforcement of a variety of data management policies, including data-dependent confinement, mandatory I/O encryption, and intellectual property management.

Malicious software is rampant on the Internet and costs billions of dollars each year. Safe and thorough analysis of malware is key to protecting vulnerable systems and cleaning those that have already been infected. Most current state-of-the-art analysis platforms run alongside the malware, increasing their detectability. This reduces the value of analysis because some malware is known to behave differently when being analyzed. Virtualization offers a compelling platform for malware analysis, with strong isolation and the ability to save and restore guest state. Current virtual machine monitors (VMMs), however, are not designed for malware analysis. Due to their complexity, they often fail to provide transparency and even expose vulnerabilities which could be exploited by the malware running inside guest system. We propose a lightweight VMM (namely MAVMM) that is designed specially for a single job: malware analysis. MAVMM does not implement unnecessary virtualization features commonly found in general purpose hypervisors, including virtual device emulation. We take advantage of hardware virtualization support to make MAVMM more simple, secure and transparent. In this paper, we describe the design and implementation of MAVMM, and the features that we can extract from programs running inside the guest OS. We evaluate our platform in three aspects: functionality, detectability and performance. We show that our system can extract useful information from malicious software, and that it is not susceptible to known virtualization detection techniques.