# Recovering Process Variables from Industrial Network Traffic via Search-Based Optimization

> PVParser recovers process-variable layouts from industrial traffic and historian records. Periodic-pattern detection locates relevant payloads; a modified Monte Carlo tree search proposes field boundaries, types and endianness, then aligns decoded sequences with observed process variables.

- Authors: Chuan Sheng; Shan Jiang; Jianming Zhao; Yu Yao
- Venue: CCS 2026
- Status: Conference accepted
- Publication / posting date: 2026 (exact date not verified)
- Date source: The official CCS 2026 accepted-paper list and arXiv v2 establish acceptance; a publication date was not verified.
- First arXiv submission: August 17, 2026
- arXiv: 2608.16403
- Reviewed arXiv version: v2
- Sources reviewed: 2026-10-20
- Added to the catalog: 2026-10-20
- Original paper: <https://arxiv.org/abs/2608.16403>
- PDF: <https://arxiv.org/pdf/2608.16403v2>
- BibTeX: <https://awesomerea.org/papers/citations/paper-pvparser.bib>
- Catalog page: <https://awesomerea.org/papers/paper-pvparser/>
- Record JSON: <https://awesomerea.org/papers/paper-pvparser/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-pvparser/index.md)

## Scope and limitations

Payload localization uses SCADA-N, SWaT and WADI; field/semantic recovery uses SWaT and WADI. Recovery depends on traffic and historian observations plus recurrent, stable plaintext byte layouts. Encrypted traffic, bit-level buses and event-driven messages whose PV lists vary are outside scope. It complements protocol dissectors.

## Why it fits this collection

Infers deployment-specific message fields and semantics from observed communication instead of supplied format specifications.

## Publication and review notes

ArXiv v2 and the CCS 2026 accepted list name four authors; v1 listed eight. The author-supplied DOI 10.1145/3830454.3846584 did not resolve at review, and no publication date or proceedings container was verified.

## Primary sources

1. <https://arxiv.org/abs/2608.16403v2>
2. <https://arxiv.org/html/2608.16403v2>
3. <https://www.sigsac.org/ccs/CCS2026/program/accepted-papers.html>

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.