mlsanityData to 14 Oct 2026

Realistic simulation of users for IT systems in cyber ranges

arXiv 2111.11785 ↗First posted 23 Nov 2021cs.AI cs.CRcited by 0

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Abstract (arXiv)

Generating user activity is a key capability for both evaluating security monitoring tools as well as improving the credibility of attacker analysis platforms (e.g., honeynets). In this paper, to generate this activity, we instrument each machine by means of an external agent. This agent combines both deterministic and deep learning based methods to adapt to different environment (e.g., multiple OS, software versions, etc.), while maintaining high performances. We also propose conditional text generation models to facilitate the creation of conversations and documents to accelerate the definition of coherent, system-wide, life scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 23 Nov 2021.

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8 of 27 references.

References · 8

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  6. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  7. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 762
  8. 2020Pandora: A Cyber Range Environment for the Safe Testing and Deployment of Autonomous Cyber Attack Toolscited by 1

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