Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications | Paper Citation Record

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This paper cites Muggah and M.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Muggah and M

Reference 1

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Observation ecb94ed6-0dee-48e0-ba0c-c4826bdc1c22 · outbound

This paper cites Dynamic vs. static cybersecurity: Which approach is more effective?.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Dynamic vs. static cybersecurity: Which approach is more effective?

Reference 2

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Observation 9d5eac62-7282-4abc-be09-0251f62a03c4 · outbound

This paper cites Does traditional security protect against modern threats?.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Does traditional security protect against modern threats?

Reference 3

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Observation fd795b31-ec4f-455b-966c-ea9eb3da1f06 · outbound

This paper cites As the threat landscape changes, traditional cybersecurity approaches need to evolve,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications As the threat landscape changes, traditional cybersecurity approaches need to evolve,

Reference 4

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Observation 26f0676c-6cfa-4811-b79b-bbf7c92bdba1 · outbound

This paper cites Cyber grand challenge,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Cyber grand challenge,

Reference 5

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Observation ab79abcb-8462-46e2-842c-016e9cda9290 · outbound

This paper cites The mayhem cyber reasoning system,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The mayhem cyber reasoning system,

Reference 6

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This paper cites Xandra: An autonomous cyber battle system for the cyber grand challenge,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Xandra: An autonomous cyber battle system for the cyber grand challenge,

Reference 7

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Observation 665549e3-209c-43e9-a3c8-57692ed5071f · outbound

This paper cites Mechanical phish: Resilient autonomous hacking,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Mechanical phish: Resilient autonomous hacking,

Reference 8

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Observation 021e2aba-26b7-413c-9431-ab10290dd7f0 · outbound

This paper cites The future of Cyber-Autonomy,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The future of Cyber-Autonomy,

Reference 9

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Observation 7637e23b-9765-43d9-b90a-b1ed1c112322 · outbound

This paper cites Orientation guide for the security of critical infrastructures,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Orientation guide for the security of critical infrastructures,

Reference 10

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Observation e6a26cc9-ab84-424f-bf94-329b7c973b9b · outbound

This paper cites Fuzzing: Chal- lenges and reflections,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Fuzzing: Chal- lenges and reflections,

Reference 11

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Observation 683dbab7-12c2-4618-a5f4-776848d064dc · outbound

This paper cites Reinforcement learning for iot security: A comprehensive survey,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Reinforcement learning for iot security: A comprehensive survey,

Reference 12

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Observation fe9052e1-3dad-4fb5-a512-1667bbffd429 · outbound

This paper cites Cyber-security and reinforcement learning — a brief survey,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Cyber-security and reinforcement learning — a brief survey,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:54.127208Z

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Observation 61bcb419-e692-4138-a1e5-6d3714a51825 · outbound

This paper cites A review of machine learning-based zero-day attack detection: Challenges and future directions,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A review of machine learning-based zero-day attack detection: Challenges and future directions,

Reference 14

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Observation dd356410-e452-43de-bcf2-52ce7ef30df5 · outbound

This paper cites an unresolved cited work.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Unresolved cited work

Reference 15

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Observation 23c1dc98-c0a1-466e-96fe-db0e40f10897 · outbound

This paper cites Reinforcement learning applications in cyber security: A review,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Reinforcement learning applications in cyber security: A review,

Reference 16

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Observation da40eb9e-d858-4e54-b83a-b4d83f951e84 · outbound

This paper cites A Multiagent CyberBattleSim for RL Cyber Operation Agents.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A Multiagent CyberBattleSim for RL Cyber Operation Agents

Reference 17

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Observation b2acef4d-3bee-4e19-95b2-7fc1fe7d18a8 · outbound

This paper cites Automated cyber defence: A review,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Automated cyber defence: A review,

Reference 18

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Observation 4d245b6c-588c-43c5-a0a9-3dbff3007d34 · outbound

This paper cites Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey

Reference 19

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Observation 98fbf916-86dd-4dfa-8cb7-a4cbd774fa92 · outbound

This paper cites The Path To Autonomous Cyber Defense.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The Path To Autonomous Cyber Defense

Reference 20

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Observation 0a649443-1dd9-4bdd-85e6-af880ae6dabb · outbound

This paper cites Autonomous Intelligent Cyber-defense Agent (AICA) Reference Architecture. Release 2.0.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Autonomous Intelligent Cyber-defense Agent (AICA) Reference Architecture. Release 2.0

Reference 21

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This paper cites Multi-agent deep reinforcement learning: a survey,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Multi-agent deep reinforcement learning: a survey,

Reference 22

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Observation af5d2004-caaf-47b6-ac61-7b0028f94f0a · outbound

This paper cites Multi-agent Reinforcement Learning: A Comprehensive Survey.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Multi-agent Reinforcement Learning: A Comprehensive Survey

Reference 23

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This paper cites an unresolved cited work.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Unresolved cited work

Reference 24

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Observation 1ecd6ec4-eecf-4aad-b681-13f389596325 · outbound

This paper cites Multi-agent reinforcement learning for cybersecurity: Approaches and challenges,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Multi-agent reinforcement learning for cybersecurity: Approaches and challenges,

Reference 25

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This paper cites Design and analysis of decentralized interactive cyber defense approach based on multi-agent coordination,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Design and analysis of decentralized interactive cyber defense approach based on multi-agent coordination,

Reference 26

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Observation 62eff5e4-c4ae-4840-b3da-ab2cd23329e9 · outbound

This paper cites A Theory of Abstraction in Reinforcement Learning.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A Theory of Abstraction in Reinforcement Learning

Reference 27

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Observation 6ff6a428-a5a0-4e4e-927c-c0c7e6c0fc70 · outbound

This paper cites Near Optimal Behavior via Approximate State Abstraction.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Near Optimal Behavior via Approximate State Abstraction

Reference 28

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This paper cites State abstraction as compression in apprenticeship learning,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications State abstraction as compression in apprenticeship learning,

Reference 29

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This paper cites Non-cooperative games,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Non-cooperative games,

Reference 30

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This paper cites Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Value-Decomposition Networks For Cooperative Multi-Agent Learning

Reference 31

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This paper cites State abstractions for lifelong reinforcement learning,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications State abstractions for lifelong reinforcement learning,

Reference 32

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This paper cites Value preserving state-action abstractions,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Value preserving state-action abstractions,

Reference 33

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Observation 2cb04fc1-aa2c-481b-86ec-9d55d9377e5d · outbound

This paper cites Reinforcement learning: An introduction,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Reinforcement learning: An introduction,

Reference 34

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Observation 132674a2-08b2-4d12-b32b-90ecf049ba9f · outbound

This paper cites Shoham and K.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Shoham and K

Reference 35

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This paper cites A game theoretic approach to decision and analysis in network intrusion detection,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A game theoretic approach to decision and analysis in network intrusion detection,

Reference 36

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This paper cites Computing optimal randomized resource allocations for massive se- curity games,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Computing optimal randomized resource allocations for massive se- curity games,

Reference 37

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Observation 0bb13fb0-f3de-4e88-9fe8-685f9d6d7853 · outbound

This paper cites A review of attacker–defender games and cyber security,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A review of attacker–defender games and cyber security,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:51.141016Z

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Observation 20426748-ce73-45f0-a272-98e8f3d37fa8 · outbound

This paper cites Dynamic games in cyber-physical security: An overview,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Dynamic games in cyber-physical security: An overview,

Reference 39

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This paper cites A pomdp approach to the dynamic defense of large-scale cyber networks,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A pomdp approach to the dynamic defense of large-scale cyber networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:50.089920Z

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This paper cites Yu and R.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Yu and R

Reference 41

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Observation a2bf5d77-2fc3-46d0-b713-14ae3edb74ab · outbound

This paper cites Multiagent Cooperation and Competition with Deep Reinforcement Learning.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Multiagent Cooperation and Competition with Deep Reinforcement Learning

Reference 42

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Observation 14835a00-abb9-47c7-9e06-4b32c9b2bd03 · outbound

This paper cites PettingZoo: Gym for Multi-Agent Reinforcement Learning.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications PettingZoo: Gym for Multi-Agent Reinforcement Learning

Reference 43

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Observation caa26b1e-3ed6-4825-b8df-55dcfb072c4e · outbound

This paper cites Multi- agent actor-critic for mixed cooperative-competitive environments,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Multi- agent actor-critic for mixed cooperative-competitive environments,

Reference 44

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This paper cites Markov games as a framework for multi- agent reinforcement learning,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Markov games as a framework for multi- agent reinforcement learning,

Reference 45

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Observation 433f8291-d7e6-42ed-b59a-90a9e52d0579 · outbound

This paper cites Counterfactual Multi-Agent Policy Gradients.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Counterfactual Multi-Agent Policy Gradients

Reference 46

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This paper cites QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Reference 47

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This paper cites Proximal Policy Optimization Algorithms.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Proximal Policy Optimization Algorithms

Reference 48

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This paper cites Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Reference 49

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This paper cites The surprising effectiveness of ppo in cooperative, multi-agent games,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The surprising effectiveness of ppo in cooperative, multi-agent games,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:49.509553Z

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Observation 2bc2e6be-22f7-4686-94b1-4f8b7e2f6eb0 · outbound

This paper cites Deterministic policy gradient algorithms,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Deterministic policy gradient algorithms,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:49.344647Z

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Observation 2c019d88-0bff-4a1f-81cd-e173715b3976 · outbound

This paper cites Cyberbattlesim,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Cyberbattlesim,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:49.177942Z

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Observation 576103ca-3a31-422c-9b90-24d69c4f3825 · outbound

This paper cites NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

Reference 53

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Observation 3b3a8c17-8a6e-44b4-9b62-fd332c91b719 · outbound

This paper cites MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:10:46.190469Z

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Observation 92c7db2b-d470-4bae-89d4-e589f727fc66 · outbound

This paper cites The StarCraft Multi-Agent Challenge.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The StarCraft Multi-Agent Challenge

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:43.252614Z

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Unavailable: canonical work link unavailable.

Observation a86f7fa6-5157-4bd6-a203-a702b346840f · outbound

This paper cites Openspiel: A framework for reinforcement learning in games,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Openspiel: A framework for reinforcement learning in games,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:48.991341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fba9c627-687a-4688-853f-fcffefd5472b · outbound

This paper cites Vine: A cyber emulation environment for mtd experimentation,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Vine: A cyber emulation environment for mtd experimentation,

Reference 57

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T14:10:46.023983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f728629-6a63-4c53-8c18-85cd4584f560 · outbound

This paper cites Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:43.536990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d76b971b-2a29-4840-9b47-e9bb3098925e · outbound

This paper cites CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:43.633319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ee9fcd7-5b99-4b88-8110-13ddb198f4eb · outbound

This paper cites OpenSpiel: A Framework for Reinforcement Learning in Games.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications OpenSpiel: A Framework for Reinforcement Learning in Games

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:43.393091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5cba973-7104-4267-9fc3-d242e87e9d22 · outbound

This paper cites Exploring the efficacy of multi-agent reinforcement learning for au- tonomous cyber defence: A cage challenge 4 perspective,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Exploring the efficacy of multi-agent reinforcement learning for au- tonomous cyber defence: A cage challenge 4 perspective,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:48.622940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97f0d783-314f-437d-920d-56e304a20b71 · outbound

This paper cites Towards an ai-powered player in cyber defence exercises,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Towards an ai-powered player in cyber defence exercises,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:48.423636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce444609-fe8e-4584-8c37-0efb52ba47af · outbound

This paper cites A survey for deep reinforcement learning based network intrusion detection,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A survey for deep reinforcement learning based network intrusion detection,

Reference 63

Resolution: verified exact; raw_fallback, observed 2026-08-07T14:10:45.767297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c6b9e9c9-2d36-4d18-a789-34f6a54f3371 · outbound

This paper cites Cyborg: An autonomous cyber operations research gym,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Cyborg: An autonomous cyber operations research gym,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:48.803503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b3fb465-29d3-4841-98fd-a50cf7650883 · outbound

This paper cites CybORG: An Autonomous Cyber Operations Research Gym.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications CybORG: An Autonomous Cyber Operations Research Gym

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:43.802178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e56e36d-f32b-46ae-9ae2-444f44f65f22 · outbound

This paper cites Csle: Cyber security learning environment,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Csle: Cyber security learning environment,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:47.840303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5cad2d08-770e-4e1b-a221-7967bcbcfec6 · outbound

This paper cites Combating advanced persistent threats: Challenges and solutions,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Combating advanced persistent threats: Challenges and solutions,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:44.447459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af88656d-54e6-4c9c-b836-7630d44ba17a · outbound

This paper cites Lateral movement (ta0008),.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Lateral movement (ta0008),

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:47.722260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f2a2327-2cef-4cdd-9a84-35ce28378a04 · outbound

This paper cites Deep q-learning based reinforcement learning approach for network intrusion detection,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Deep q-learning based reinforcement learning approach for network intrusion detection,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:48.220895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 115bf963-b166-414b-87a5-242bec0624bd · outbound

This paper cites Exploitation of remote services (t1210),.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Exploitation of remote services (t1210),

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:47.303119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02621236-e1f8-451d-8f82-dfe8b6be9aa8 · outbound

This paper cites Cy- bershield: A competitive simulation environment for training ai in cybersecurity,.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Cy- bershield: A competitive simulation environment for training ai in cybersecurity,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:48.023140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 937242ae-a1f3-4129-92b6-972b58465a0e · outbound

This paper cites Farsighted Risk Mitigation of Lateral Movement Using Dynamic Cognitive Honeypots.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Farsighted Risk Mitigation of Lateral Movement Using Dynamic Cognitive Honeypots

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:10:45.257838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13f801da-c42e-40f7-98dd-b4820fee3e50 · outbound

This paper cites Os credential dumping (t1003),.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Os credential dumping (t1003),

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:47.533296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79ba06a1-f19b-4172-a6c9-559343b2b65e · outbound

This paper cites Privilege escalation (ta0004),.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Privilege escalation (ta0004),

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:47.131494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a4950266-b309-4b29-885c-3877313e1841 · outbound

This paper cites Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:42.258052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6219ee9-f649-48ec-958a-4ade2951d41b · outbound

This paper cites Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection

Reference 2021

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:10:45.551198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c273e565-dba9-418f-a06a-1c828020ccd6 · outbound

This paper cites The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:42.885123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c080564-5c4b-4180-98ff-f463f26ad388 · outbound

This paper cites Available: https://www.mdpi.com/2073-4336/15/4/28.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Available: https://www.mdpi.com/2073-4336/15/4/28

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:50.796686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers