Adam Bates

Jonathan Oliver, Raghav Batta, Adam Bates et al.

"Alert fatigue" is one of the biggest challenges faced by the Security Operations Center (SOC) today, with analysts spending more than half of their time reviewing false alerts. Endpoint detection products raise alerts by pattern matching on event telemetry against behavioral rules that describe potentially malicious behavior, but can suffer from high false positives that distract from actual attacks. While alert triage techniques based on data provenance may show promise, these techniques can take over a minute to inspect a single alert, while EDR customers may face tens of millions of alerts per day; the current reality is that these approaches aren't nearly scalable enough for production environments. We present Carbon Filter, a statistical learning based system that dramatically reduces the number of alerts analysts need to manually review. Our approach is based on the observation that false alert triggers can be efficiently identified and separated from suspicious behaviors by examining the process initiation context (e.g., the command line) that launched the responsible process. Through the use of fast-search algorithms for training and inference, our approach scales to millions of alerts per day. Through batching queries to the model, we observe a theoretical maximum throughput of 20 million alerts per hour. Based on the analysis of tens of million alerts from customer deployments, our solution resulted in a 6-fold improvement in the Signal-to-Noise ratio without compromising on alert triage performance.

Xueyuan Han, Thomas Pasquier, Adam Bates et al.

Advanced Persistent Threats (APTs) are difficult to detect due to their "low-and-slow" attack patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based APT detector that effectively leverages data provenance analysis. From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without pre-defined attack signatures. Using a graph sketching technique, it summarizes long-running system execution with space efficiency to combat slow-acting attacks that take place over a long time span. UNICORN further improves its detection capability using a novel modeling approach to understand long-term behavior as the system evolves. Our evaluation shows that UNICORN outperforms an existing state-of-the-art APT detection system and detects real-life APT scenarios with high accuracy.

Thomas Pasquier, Xueyuan Han, Thomas Moyer et al.

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.

Adam Bates, Kevin Butler, Alin Dobra et al.

Data provenance is a valuable tool for detecting and preventing cyber attack, providing insight into the nature of suspicious events. For example, an administrator can use provenance to identify the perpetrator of a data leak, track an attacker's actions following an intrusion, or even control the flow of outbound data within an organization. Unfortunately, providing relevant data provenance for complex, heterogenous software deployments is challenging, requiring both the tedious instrumentation of many application components as well as a unified architecture for aggregating information between components. In this work, we present a composition of techniques for bringing affordable and holistic provenance capabilities to complex application workflows, with particular consideration for the exemplar domain of web services. We present DAP, a transparent architecture for capturing detailed data provenance for web service components. Our approach leverages a key insight that minimal knowledge of open protocols can be leveraged to extract precise and efficient provenance information by interposing on application components' communications, granting DAP compatibility with existing web services without requiring instrumentation or developer cooperation. We show how our system can be used in real time to monitor system intrusions or detect data exfiltration attacks while imposing less than 5.1 ms end-to-end overhead on web requests. Through the introduction of a garbage collection optimization, DAP is able to monitor system activity without suffering from excessive storage overhead. DAP thus serves not only as a provenance-aware web framework, but as a case study in the non-invasive deployment of provenance capabilities for complex applications workflows.