Identifying the root cause and impact of a system intrusion remains a foundational challenge in computer security. Digital provenance provides a detailed history of the flow of information within a computing system, connecting suspicious events to their root causes. Although existing provenance-based auditing techniques provide value in forensic analysis, they assume that such analysis takes place only retrospectively. Such post-hoc analysis is insufficient for realtime security applications, moreover, even for forensic tasks, prior provenance collection systems exhibited poor performance and scalability, jeopardizing the timeliness of query responses. We present CamQuery, which provides inline, realtime provenance analysis, making it suitable for implementing security applications. CamQuery is a Linux Security Module that offers support for both userspace and in-kernel execution of analysis applications. We demonstrate the applicability of CamQuery to a variety of runtime security applications including data loss prevention, intrusion detection, and regulatory compliance. In evaluation, we demonstrate that CamQuery reduces the latency of realtime query mechanisms, while imposing minimal overheads on system execution. CamQuery thus enables the further deployment of provenance-based technologies to address central challenges in computer security.
Park al @cite_7 formalise the notion of provenance-based access control (PBAC) systems along three dimensions: 1) the type of data used to make decisions (observed vs. disclosed provenance @cite_64 ); 2) object dependencies (information flow between objects) vs. user dependencies (information flow between users); and 3) whether policies are available to the system or learnt through the traversal of provenance graphs; , like most PBAC systems in the literature ( @cite_13 @cite_108 ), uses observed provenance, although it could be augmented by disclosed provenance. Layering of provenance systems @cite_100 could enable such a capability, although we are not aware of any layered PBAC enforcement model. We plan to explore this approach in future work, with both application level @cite_100 and network level provenance @cite_44 .
@cite_64 @cite_7 @cite_108 @cite_44 @cite_100 @cite_13
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Existence of data provenance information in a system raises at least two security-related issues. One is how provenance data can be used to enhance security in the system and the other is how to protect provenance data which might be more sensitive than the data itself. Recent data provenance-related access control literature mainly focuses on the latter issue of protecting provenance data. In this paper, we propose a novel provenance-based access control model that addresses the former objective. Using provenance data for access control to the underlying data facilitates additional capabilities beyond those available in traditional access control models. We utilize a notion of dependency as the key foundation for access control policy specification. Dependency-based policy provides simplicity and effectiveness in policy specification and access control administration. We show our model can support dynamic separation of duty, workflow control, origin-based control, and object versioning. The proposed model identifies essential components and concepts and provides a foundational base model for provenance-based access control. We further discuss possible extensions of the proposed base model for enhanced access controls. Digital provenance describes the ancestry or history of a digital object. Most existing provenance systems, however, operate at only one level of abstraction: the system call layer, a workflow specification, or the high-level constructs of a particular application. The provenance collectable in each of these layers is different, and all of it can be important. Single-layer systems fail to account for the different levels of abstraction at which users need to reason about their data and processes. These systems cannot integrate data provenance across layers and cannot answer questions that require an integrated view of the provenance. We have designed a provenance collection structure facilitating the integration of provenance across multiple levels of abstraction, including a workflow engine, a web browser, and an initial runtime Python provenance tracking wrapper. We layer these components atop provenance-aware network storage (NFS) that builds upon a Provenance-Aware Storage System (PASS). We discuss the challenges of building systems that integrate provenance across multiple layers of abstraction, present how we augmented systems in each layer to integrate provenance, and present use cases that demonstrate how provenance spanning multiple layers provides functionality not available in existing systems. Our evaluation shows that the overheads imposed by layering provenance systems are reasonable. As organizations become increasingly reliant on cloud computing for servicing their data storage requirements, the need to govern access control at finer granularities becomes particularly important. This challenge is increased by the lack of policy supporting data migration across geographic boundaries and through organizations with divergent regulatory policies. In this paper, we present an architecture for secure and distributed management of provenance, enabling its use in security-critical applications. Provenance, a metadata history detailing the derivation of an object, contains information that allows for expressive, policy-independent access control decisions. We consider how to manage and validate the metadata of a provenance-aware cloud system, and introduce protocols that allow for secure transfer of provenance metadata between end hosts and cloud authorities. Using these protocols, we develop a provenance-based access control mechanism for Cumulus cloud storage, capable of processing thousands of operations per second on a single deployment. Through the introduction of replicated components, we achieve overhead costs of just 14 , demonstrating that provenance-based access control is a practical and scalable solution for the cloud. As increasing amounts of valuable information are produced and persist digitally, the ability to determine the origin of data becomes important. In science, medicine, commerce, and government, data provenance tracking is essential for rights protection, regulatory compliance, management of intelligence and medical data, and authentication of information as it flows through workplace tasks. In this paper, we show how to provide strong integrity and confidentiality assurances for data provenance information. We describe our provenance-aware system prototype that implements provenance tracking of data writes at the application layer, which makes it extremely easy to deploy. We present empirical results that show that, for typical real-life workloads, the run-time overhead of our approach to recording provenance with confidentiality and integrity guarantees ranges from 1 -13 .