Provenance is the representation of a system execution as a directed acyclic graph. Whole-system provenance graph, representing the execution of an entire system from initialization to shut down, can be comprised of millions of graph elements. In this talk, I will present my work on the development of a provenance-based intrusion detection system. I will discuss the development of the stack from the kernel-level capture mechanism to the algorithm used to perform intrusion detection. Finally, I will discuss planned future work and areas of potential collaborations.
Provenance is the representation of a system execution as a directed acyclic graph. Whole-system provenance graph, representing the execution of an entire system from initialisation to shut down, can be comprised of millions of graph elements. In this talk, I will present my work on the development of a provenance-based intrusion detection system. I will discuss the development of the stack from the kernel-level capture mechanism to the algorithm used to perform intrusion detection. Finally, I will discuss planned future work and areas of potential collaborations.
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.
Intrusion detection is an arms race; attackers evade intrusion detection systems by developing new attack vectors to sidestep known defense mechanisms. Provenance provides a detailed, structured history of the interactions of digital objects within a system. It is ideal for intrusion detection, because it offers a holistic, attack-vector-agnostic view of system execution. As such, provenance graph analysis fundamentally strengthens detection robustness. We discuss the opportunities and challenges associated with provenance-based intrusion detection and provide insights based on our experience building such systems.
There is a consensus that understanding data provenance, the origin and history of digital artifacts, is important. Whole-system provenance systems are capture mechanisms aimed at recording all information flows in an operating system. Such systems have been the subject of recent attention from the research security community. However, whole-system provenance as yet to make a significant impact outside of academic circles. In this talk, I will present our work on CamFlow an open-source whole-system provenance implementation for Linux, and briefly introduce ongoing work on provenance-based intrusion detection as an application example. I will discuss the technical barriers to practical whole-system provenance we aimed to overcome, and those left to address.
There is a consensus that understanding data provenance, the origin and history of digital artifacts, is important. Whole-system provenance systems are capture mechanisms aimed at recording all information flows in an operating system. Such systems have been the subject of recent attention from the research security community. However, whole-system provenance as yet to make a significant impact outside of academic circles. In this talk, I will present our work on CamFlow an open-source whole-system provenance implementation for Linux, and briefly introduce ongoing work on provenance-based intrusion detection as an application example. I will discuss the technical barriers to practical whole-system provenance we aimed to overcome, and those left to address.
Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of scientific experiments. We present CamFlow, a whole-system provenance capture mechanism that integrates easily into a PaaS offering. While there have been several prior whole-system provenance systems that captured a comprehensive, systemic and ubiquitous record of a system’s behavior, none have been widely adopted. They either A) impose too much overhead, B) are designed for long-outdated kernel releases and are hard to port to current systems, C) generate too much data, or D) are designed for a single system. CamFlow addresses these shortcoming by: 1) leveraging the latest kernel design advances to achieve efficiency; 2) using a self-contained, easily maintainable implementation relying on a Linux Security Module, NetFilter, and other existing kernel facilities; 3) providing a mechanism to tailor the captured provenance data to the needs of the application; and 4) making it easy to integrate provenance across distributed systems. The provenance we capture is streamed and consumed by tenant-built auditor applications. We illustrate the usability of our implementation by describing three such applications: demonstrating compliance with data regulations; performing fault/intrusion detection; and implementing data loss prevention. We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.