Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools
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Abstract (arXiv)
Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry: (1) overlooking client-side overhead; (2) imbalanced alarm triage cost and interpretation cost; and (3) excessive server-side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2023.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 4.
5 papers.
- 2026Benchmarking and Exploring the Capabilities of LLMs for Attack Investigationscited by 2
- 2026PIDSMaker: Building and Evaluating Provenance-based Intrusion Detection Systemscited by 1
- 2024RAPID: Robust APT Detection and Investigation Using Context-Aware Deep Learningcited by 0
- 2026GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classificationcited by 0
- 2026How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detectioncited by 0
- 2026How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection2 Aug 2026
- 2026Benchmarking and Exploring the Capabilities of LLMs for Attack Investigations9 Jun 2026
- 2026GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification8 May 2026
- 2026PIDSMaker: Building and Evaluating Provenance-based Intrusion Detection Systems30 Jan 2026
- 2024RAPID: Robust APT Detection and Investigation Using Context-Aware Deep Learning8 Jun 2024