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Paper Citation Record · LEDGER

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2607.22009.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.22009 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:15:20.662763Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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External citation measurements

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Outbound references

Observation 13f276de-66be-4938-89df-01dc383c29a7 · outbound

This paper cites an unresolved cited work.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Unresolved cited work

Reference 1

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Observation 5be9b628-0c25-40f5-a3b8-776d131e21f8 · outbound

This paper cites Cyber maintenance policy optimization via adap- tive learning.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Cyber maintenance policy optimization via adap- tive learning

Reference 2

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Observation f6fe182f-f97a-48f4-abf2-ae5121b0d488 · outbound

This paper cites Adaptive submodular maximization in bandit setting.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Adaptive submodular maximization in bandit setting

Reference 7

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Observation 349fcf67-2659-43cc-9871-92bd8225977b · outbound

This paper cites Optimal surveillance of covert net- works by minimizing inverse geodesic length.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Optimal surveillance of covert net- works by minimizing inverse geodesic length

Reference 8

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Observation 7b9315b4-c711-460e-9f26-ac6ec99ce88f · outbound

This paper cites Limited Query Graph Connectivity Test.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Limited Query Graph Connectivity Test

Reference 10

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Observation d3d04cda-c662-414e-a3c5-2288a7d8cdb2 · outbound

This paper cites Coalitional security games.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Coalitional security games

Reference 11

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Observation 116db2db-92c0-4b6b-88d7-edb152883acc · outbound

This paper cites A Formal Model for Credential Hopping Attacks.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening A Formal Model for Credential Hopping Attacks

Reference 12

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Observation a21facb5-fa6e-460d-a14f-9eb71defd7f6 · outbound

This paper cites Ranking attack graphs.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Ranking attack graphs

Reference 15

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Observation ed8da456-1f79-44a5-bfdf-1093c9e75d77 · outbound

This paper cites Prioritized Experience Replay.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Prioritized Experience Replay

Reference 20

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Observation f52f89ab-a0df-4180-a8d5-537b4d34f001 · outbound

This paper cites Jump- start reinforcement learning.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Jump- start reinforcement learning

Reference 24

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Observation 1d761895-812c-4c9f-a750-c50878ae8fe0 · outbound

This paper cites Deep Reinforcement Learning for Cost-Effective Medical Diagnosis.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

Reference 25

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Observation 9fb5c736-a2c6-4f14-91e2-47c3f3e1567a · outbound

This paper cites Urban security: Game-theoretic resource allocation in networked domains.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Urban security: Game-theoretic resource allocation in networked domains

Reference 26

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Observation b1a48ed1-fb7f-4e0d-80f3-493a671d9df1 · outbound

This paper cites Optimizing cyber defense in dynamic active directories through reinforcement learning.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Optimizing cyber defense in dynamic active directories through reinforcement learning

Reference 33

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Observation 75874d5f-ddfe-4014-96cf-ee8af5c68717 · outbound

This paper cites Deep Generative Models to Extend Active Directory Graphs with Honeypot Users.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Deep Generative Models to Extend Active Directory Graphs with Honeypot Users

Reference 34

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Observation e7124a5e-54b8-4d3d-b797-4f5a1c29b4a5 · outbound

This paper cites Curriculum learning for multilevel bud- geted combinatorial problems.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Curriculum learning for multilevel bud- geted combinatorial problems

Reference 35

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Observation 675e2e00-7b8c-4ceb-a433-41ff977fb2dd · outbound

This paper cites Optimizing Cyber Response Time on Temporal Active Directory Networks Using Decoys.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Optimizing Cyber Response Time on Temporal Active Directory Networks Using Decoys

Reference 99

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Observation 6636a9f9-1776-435c-b6ff-c1d2c613551c · outbound

This paper cites Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization

Reference 167

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Observation 406d8430-2d0c-4105-9d0e-a070b86eef4b · outbound

This paper cites Soft Actor-Critic for Discrete Action Settings.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Soft Actor-Critic for Discrete Action Settings

Reference 345

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Observation 429c454f-e648-4bbb-9cdb-8ca82b2ee3a0 · outbound

This paper cites Optimal ids sensor placement and alert pri- oritization using attack graphs.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Optimal ids sensor placement and alert pri- oritization using attack graphs

Reference 608

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Observation 5dab87f8-3d15-4b37-9bf4-102d7f0afa59 · outbound

This paper cites Cybersecurity Data Sources for Dynamic Network Re- search.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Cybersecurity Data Sources for Dynamic Network Re- search

Reference 690

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Observation b1af4496-68d4-474d-bf47-083b827be4c5 · outbound

This paper cites Robust curriculum learning: from clean label detection to noisy label self-correction.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Robust curriculum learning: from clean label detection to noisy label self-correction

Reference 1750

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Observation 61b5daf5-b5fc-4fcd-a002-a0c4e70b8838 · outbound

This paper cites Harnessing the power of deception in attack graph-based security games.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Harnessing the power of deception in attack graph-based security games

Reference 2022

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Observation cb4c27f2-cbc0-4b13-a535-a7b7b6f76d9f · outbound

This paper cites To delay or not: temporal vaccinationgamesonnetworks.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening To delay or not: temporal vaccinationgamesonnetworks

Reference 2025

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Observation 75689532-4061-4bfb-8a74-a6e73a1ee0f3 · outbound

This paper cites Beyond adaptive submodularity: Approxi- mation guarantees of greedy policy with adaptive submodularity ratio.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Beyond adaptive submodularity: Approxi- mation guarantees of greedy policy with adaptive submodularity ratio

Reference 3252

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Observation 950d8ad0-a759-4bec-b049-c93e81fceab3 · outbound

This paper cites Resource-Limited Network Security Games with General Contagious Attacks.

Practical Graph Optimisation and AI-Driven Models for Active Directory Security Hardening Resource-Limited Network Security Games with General Contagious Attacks

Reference 5142

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Pith citing papers

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