Paper Citation Record · LEDGER

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning

As of 4 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2604.04442.

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

pith.paper-citation-record.v1
2604.04442 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T20:15:46.454806Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T15:33:59.591966Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-05-11T11:01:03.130467Z

Reference resolution

62 of 62 outbound references displayed

verified exact10
verified fuzzy49
unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 15c7da19-73b2-4192-ad9a-b2fab378bfa5 · outbound

This paper cites Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.383325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0b4d6c27-9286-40dc-8bfb-f44c88d35d2f · outbound

This paper cites Overview of smartphone security: Attack and defense techniques.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Overview of smartphone security: Attack and defense techniques

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.470972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 613a9ea7-c81d-4cfa-9dd9-9be03f6c7eaa · outbound

This paper cites Smart hpa: A resource-efficient horizontal pod auto-scaler for microservice archi- tectures.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Smart hpa: A resource-efficient horizontal pod auto-scaler for microservice archi- tectures

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.493178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f9a98530-30a7-45a7-a474-0f7884c50488 · outbound

This paper cites Towards resource-efficient reactive and proactive auto-scaling for microservice architectures.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Towards resource-efficient reactive and proactive auto-scaling for microservice architectures

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.483689Z

Source-reported events for the cited work

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Observation 1fd94993-38e4-4904-bcea-f4a003d6d565 · outbound

This paper cites Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.387772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1edf1fa4-0077-4c81-b45b-09561e549142 · outbound

This paper cites Regimefolio: A regime aware ml system for sectoral portfolio optimization in dynamic markets.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Regimefolio: A regime aware ml system for sectoral portfolio optimization in dynamic markets

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.564749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 914512fc-adad-46b9-a59f-fb16cbf3d049 · outbound

This paper cites 3s-trader: A multi-llm framework for adaptive stock scoring, strategy, and selection in portfolio optimization.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning 3s-trader: A multi-llm framework for adaptive stock scoring, strategy, and selection in portfolio optimization

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.413816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e38abf92-43be-4104-83ca-7bbbbe98cad8 · outbound

This paper cites Australian bushfire intelligence with ai-driven environmental analytics.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Australian bushfire intelligence with ai-driven environmental analytics

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.419217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 646edf28-5aa4-4c1c-8749-36dc999ade97 · outbound

This paper cites A survey of security challenges in cloud-based SCADA systems.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A survey of security challenges in cloud-based SCADA systems

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.566760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5fb5c669-f9ec-4820-9baa-145a09f45efb · outbound

This paper cites A survey on security issues in smart grids.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A survey on security issues in smart grids

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.546103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b6a812df-1503-44d1-8819-9db5792f3532 · outbound

This paper cites A review on c3i systems’ security: Vulnerabilities, attacks, and countermeasures.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A review on c3i systems’ security: Vulnerabilities, attacks, and countermeasures

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.562650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1ee6d2e7-e7d6-456a-a746-b819f785395a · outbound

This paper cites Microservice vulnerability analysis: A literature review with empirical insights.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Microservice vulnerability analysis: A literature review with empirical insights

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.537873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ad94cce7-ed7a-4e5c-b655-63190c42da80 · outbound

This paper cites Living off the land and fileless attack techniques.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Living off the land and fileless attack techniques

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.491036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 390d71f0-1535-4c1b-9282-c3793c4389eb · outbound

This paper cites A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.570806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9c6c410f-272b-44c5-ab13-65aef5a9d801 · outbound

This paper cites Chatnvd: Advancing cybersecurity vulnerability assessment with large language models.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Chatnvd: Advancing cybersecurity vulnerability assessment with large language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.479529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1073791f-0ea1-4564-989b-1efd31776a40 · outbound

This paper cites Towards deep learning enabled cybersecurity risk assessment for microservice archi- tectures.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Towards deep learning enabled cybersecurity risk assessment for microservice archi- tectures

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.466881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1a27597a-2aaa-4905-ab1d-bfc913263ab0 · outbound

This paper cites Deep reinforcement learning for cyber security.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Deep reinforcement learning for cyber security

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.540146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f1866209-c913-4261-9951-2f9668f3f8a3 · outbound

This paper cites Enhancing security and energy efficiency of cyber-physical systems using deep reinforcement learning.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Enhancing security and energy efficiency of cyber-physical systems using deep reinforcement learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.485669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 26c9e7b4-be44-419c-ad66-1e5e209f8fad · outbound

This paper cites Kott, Ed.,Autonomous Intelligent Cyber Defense Agent (AICA), ser.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Kott, Ed.,Autonomous Intelligent Cyber Defense Agent (AICA), ser

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.558758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 16196020-a484-42a1-8191-3ac44a62a099 · outbound

This paper cites Optimizing cyber defense in dynamic active directories through re- inforcement learning.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Optimizing cyber defense in dynamic active directories through re- inforcement learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.556871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c3507f96-5ef9-4e24-9e63-65fa86fbdbd0 · outbound

This paper cites Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.403898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0eead18c-a3cc-42f5-905c-07dd1e0ced5f · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.473366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 90f09be0-59b3-4448-9a75-38b33349dbfa · outbound

This paper cites Peeking inside the black-box: A survey on explainable artificial intelligence (xai).

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Peeking inside the black-box: A survey on explainable artificial intelligence (xai)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.507301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2194b677-d2e4-4766-ba55-483f2dfab3a7 · outbound

This paper cites Practical black-box attacks against machine learning.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Practical black-box attacks against machine learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.572722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation df12c636-ab70-46d5-8c0e-28b80396259f · outbound

This paper cites Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.475299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 241635a6-72f8-47ea-a71a-cd5083caea4b · outbound

This paper cites Anomaly de- tection in vehicular networks using causality-aware graph convolutional networks (CA-GCN).

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Anomaly de- tection in vehicular networks using causality-aware graph convolutional networks (CA-GCN)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.495450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a5d0a265-6a03-4baf-aa0f-692dd76565b2 · outbound

This paper cites Pearl,Causality: Models, Reasoning, and Inference, 2nd ed.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Pearl,Causality: Models, Reasoning, and Inference, 2nd ed

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.497417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 056d45ed-19df-4084-a009-46f2feb4363a · outbound

This paper cites Robust Partial Least Squares Using Low Rank and Sparse Decomposition.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Robust Partial Least Squares Using Low Rank and Sparse Decomposition

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.400394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 68a77f97-e089-4cc0-af1f-d3f01ea49bce · outbound

This paper cites A comprehensive review of explainable AI in cybersecurity: Decoding the black box.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A comprehensive review of explainable AI in cybersecurity: Decoding the black box

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.499392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93d44442-fc0d-4970-96f7-1eece01156fe · outbound

This paper cites Scalar: Self-calibrating adaptive latent attention representation learning.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Scalar: Self-calibrating adaptive latent attention representation learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.568795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 086091a2-4ac6-40cb-8efa-36a1b5c051ad · outbound

This paper cites AutoGen: Enabling next-gen LLM applications via multi-agent conversation.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning AutoGen: Enabling next-gen LLM applications via multi-agent conversation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.515507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f2320438-29fa-4251-a729-0ce52246601b · outbound

This paper cites Intelligent multi-agent collaboration model for smart home IoT security.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Intelligent multi-agent collaboration model for smart home IoT security

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.481700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 578784f9-46b2-4d91-a9d0-705aee9c21fc · outbound

This paper cites Co-evolutionary defence of active directory attack graphs via gnn-approximated dynamic pro- gramming.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Co-evolutionary defence of active directory attack graphs via gnn-approximated dynamic pro- gramming

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.423564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0d9b9357-d1e6-4f9c-8083-e1502ab51f26 · outbound

This paper cites Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:00:19.842991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation acc1bcf4-74bb-422f-9dfd-75a6a17fce39 · outbound

This paper cites A survey on immersive cyber situational awareness systems.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A survey on immersive cyber situational awareness systems

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.574630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 57a5959b-de79-456d-b03a-0a4704c3348f · outbound

This paper cites Alpcan and T.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Alpcan and T

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.505264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3695d2fe-0bcf-40d8-8dee-048b16938486 · outbound

This paper cites Security and privacy for green IoT-based agriculture: Review, blockchain solu- tions, and challenges.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Security and privacy for green IoT-based agriculture: Review, blockchain solu- tions, and challenges

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.525612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b1f1c5ca-1562-4a48-9d2e-6540febf81b2 · outbound

This paper cites Network intrusion detection: An optimized deep learning approach using big data analytics.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Network intrusion detection: An optimized deep learning approach using big data analytics

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.550597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a059daf5-6d79-4ff7-811c-a211137e7f9b · outbound

This paper cites A bidirectional LSTM deep learning approach for intrusion detection.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A bidirectional LSTM deep learning approach for intrusion detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.533794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 08de8c59-8b93-456d-8d78-2ad10019d35e · outbound

This paper cites Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.535872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eeb2ce17-4484-4000-bfe9-426d0746225c · outbound

This paper cites Spirtes, C.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Spirtes, C

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.521681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f31d3872-1abd-420e-9630-932dc24823c9 · outbound

This paper cites Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.531757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 708d9536-5477-485b-bf55-668721ef94b7 · outbound

This paper cites The Future of AI: Exploring the Potential of Large Concept Models.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning The Future of AI: Exploring the Potential of Large Concept Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.407449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b86649de-9927-40f4-b3d1-7fcedba759ce · outbound

This paper cites What skills do cybersecurity professionals need?.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning What skills do cybersecurity professionals need?

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.523664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bdc95a9a-18cc-49e5-ad64-822d0a716f90 · outbound

This paper cites Intrusion detection using hybridized meta-heuristic techniques with weighted XGBoost classifier.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Intrusion detection using hybridized meta-heuristic techniques with weighted XGBoost classifier

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.511391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b30c78f4-01f4-45b6-9f7f-0f529695351e · outbound

This paper cites A new intrusion detection system based on moth-flame optimizer algorithm.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A new intrusion detection system based on moth-flame optimizer algorithm

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.513389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 404bf761-d937-4b7d-840e-0355f9a5044d · outbound

This paper cites Goodfellow, Y.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Goodfellow, Y

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.509344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 38cefffa-9d30-4202-8097-09ef0235cabb · outbound

This paper cites Machine Learning Driven Smishing Detection Framework for Mobile Security.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Machine Learning Driven Smishing Detection Framework for Mobile Security

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.391135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d6a3bf45-fb28-41b5-a79f-4b871087e32a · outbound

This paper cites Review of artificial intel- ligence for enhancing intrusion detection in the internet of things.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Review of artificial intel- ligence for enhancing intrusion detection in the internet of things

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.519788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c29cc42d-c3c2-47b4-ae23-1cabad66538c · outbound

This paper cites An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.544124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 26c84f0b-bd66-4785-ae43-831ba540150b · outbound

This paper cites An improved random forest based on the classification accuracy and correlation measurement of decision trees.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning An improved random forest based on the classification accuracy and correlation measurement of decision trees

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.527690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 82b72f0a-e6da-4e46-93de-981b051a013e · outbound

This paper cites Malware detection issues, challenges, and future directions: A survey.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Malware detection issues, challenges, and future directions: A survey

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.548460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ea551b6f-a575-4d4d-8edb-f0c2e55ea4bf · outbound

This paper cites Verified models and reference implementations for the TLS 1.3 standard candidate.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Verified models and reference implementations for the TLS 1.3 standard candidate

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.501419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0c3e1a33-1be8-49dc-9d99-ee23fcca7a85 · outbound

This paper cites Explanation in artificial intelligence: Insights from the social sciences.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Explanation in artificial intelligence: Insights from the social sciences

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.469043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d5f48d20-c0f8-4234-b1d0-a9062afa8960 · outbound

This paper cites Counterfactual explanations and algorithmic recourses for machine learning: A review.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Counterfactual explanations and algorithmic recourses for machine learning: A review

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.529762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 852c2cda-8af0-4fda-b1ff-0779e2b01d7e · outbound

This paper cites Human-in-the-loop machine learn- ing: A state of the art.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Human-in-the-loop machine learn- ing: A state of the art

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.503382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e37737d4-cee8-4458-9a77-d2d3424ba2b8 · outbound

This paper cites Shoham and K.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Shoham and K

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.541997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eabcfb8d-c690-4cd9-ba9a-aea41c57f82e · outbound

This paper cites A survey and critique of multiagent deep reinforcement learning.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning A survey and critique of multiagent deep reinforcement learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.560712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 61d36f1c-5fcd-40b8-9fdf-4d56c54fd05e · outbound

This paper cites an unresolved cited work.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-05-16T01:57:06.477397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 39da995d-5bfb-4d65-bf69-834358fc1d82 · outbound

This paper cites an unresolved cited work.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-16T01:57:06.517781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a30716f2-b0ee-4e6f-a766-b75f27dbced0 · outbound

This paper cites an unresolved cited work.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-05-16T01:57:06.554902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 07813db4-903e-4ece-90f0-cdc8075dff51 · outbound

This paper cites CICIoT2023: A real-time dataset and benchmark for large- scale attacks in IoT environment.

Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning CICIoT2023: A real-time dataset and benchmark for large- scale attacks in IoT environment

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T01:57:06.553031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 0b6620da-54f7-4bb9-8f83-c7c4dc91ebab · inbound

Comparative Analysis of Large Language Models in Healthcare cites this paper.

Comparative Analysis of Large Language Models in Healthcare Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-11T10:16:07.967349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9a71d5eb-517d-41be-8640-2cfaf2e5d5da · inbound

AgenticVM: Agentic AI for Adaptive Software Vulnerability Management cites this paper.

AgenticVM: Agentic AI for Adaptive Software Vulnerability Management Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-11T11:01:03.133579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.