Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 24 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

A reinforcement learning-driven autonomous threat hunting agent with hypothesis-guided graph traversal for APT campaign discovery

Related

  1. Context-Guided Traversal with Deep Reinforcement Learning for Suspicious Subgraph Detection in Cryptocurrency Transaction Graphs
    Seok-Jun Buu, Hae-Jung Kim
    MathematicsPeer-reviewedCC BY 4.0General mathematicsCrossref
  2. ETPP: An Efficient Traversal Path Planning Framework for Autonomous Robot Exploration in Unknown Environments
    Lingli Yu, Chengxiang Peng, Jiawei Luo +2
    Sensors (Basel, Switzerland)Peer-reviewedCC BYFull textEurope PMC
  3. Deep reinforcement learning-driven discovery of a MsbA-targeted small-molecule antibiotic for the treatment of Acinetobacter baumannii infection
    L. Wang, B. Jing, Q. Liu +8
    bioRxivPreprintAll rights reservedFull textGeneral biochemistrybioRxiv
  4. Reinforcement learning enables autonomous microrobot navigation and intervention in simulated blood capillaries
    Jannik Drotleff, Paul Hohenberger, Samuel Tovey +4
    Scientific ReportsPeer-reviewedCC BY 4.0Full textCrossref
  5. Autonomous Droplet Navigation via Model-Based Reinforcement Learning
    Rajneesh Anand, Mayuresh V. Kothare
    arXivPreprintarXiv licenceFull textFluid dynamicsarXiv
BibTeX
@article{alnajim2026reinforcement,
  title = {{A reinforcement learning-driven autonomous threat hunting agent with hypothesis-guided graph traversal for APT campaign discovery}},
  author = {Abdullah Abdulkarim Alnajim},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-76851-9},
  url = {https://www.nature.com/articles/s41598-026-76851-9},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - A reinforcement learning-driven autonomous threat hunting agent with hypothesis-guided graph traversal for APT campaign discovery
AU  - Abdullah Abdulkarim Alnajim
PY  - 2026
DA  - 2026/10/28
JO  - Scientific Reports
DO  - 10.1038/s41598-026-76851-9
UR  - https://www.nature.com/articles/s41598-026-76851-9
SN  - 2045-2322
ER  -