GAT-APG: Graph Attention Network-Based Attack Path Generation for Security Simulation
Existing attack path generation methods face limitations in dynamic simulation environments due to their reliance on static network models and computational inefficiencies when network configurations change frequently. This study proposes GAT-APG, a reinforcement learning framework that combines Gra...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11131165/ |
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| author | Min Geun Song Jaewoong Choi Huy Kang Kim |
| author_facet | Min Geun Song Jaewoong Choi Huy Kang Kim |
| author_sort | Min Geun Song |
| collection | DOAJ |
| description | Existing attack path generation methods face limitations in dynamic simulation environments due to their reliance on static network models and computational inefficiencies when network configurations change frequently. This study proposes GAT-APG, a reinforcement learning framework that combines Graph Attention Networks with policy gradient methods to generate adaptive attack paths for security simulation. The approach employs node-centric vulnerability assessment that transforms network traffic data into vulnerability metrics, enabling adaptation to network changes without complete graph recalculation. Experimental validation on controlled graphs and the Kyoto dataset demonstrates competitive performance, achieving 93% accuracy against brute-force methods and showing 69.1% exact matches with Dijkstra’s algorithm on real-world topologies. The framework provides a simulation-ready environment for vulnerability assessment and defensive planning in dynamic networks. |
| format | Article |
| id | doaj-art-8491cf2045184cfc839831ab48c0f00c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8491cf2045184cfc839831ab48c0f00c2025-08-25T23:18:44ZengIEEEIEEE Access2169-35362025-01-011314738314739610.1109/ACCESS.2025.360066911131165GAT-APG: Graph Attention Network-Based Attack Path Generation for Security SimulationMin Geun Song0https://orcid.org/0009-0009-1133-9783Jaewoong Choi1https://orcid.org/0009-0007-6828-2760Huy Kang Kim2https://orcid.org/0000-0002-0760-8807Graduate School of Information Security, Korea University, Seoul, Republic of KoreaGraduate School of Information Security, Korea University, Seoul, Republic of KoreaGraduate School of Information Security, Korea University, Seoul, Republic of KoreaExisting attack path generation methods face limitations in dynamic simulation environments due to their reliance on static network models and computational inefficiencies when network configurations change frequently. This study proposes GAT-APG, a reinforcement learning framework that combines Graph Attention Networks with policy gradient methods to generate adaptive attack paths for security simulation. The approach employs node-centric vulnerability assessment that transforms network traffic data into vulnerability metrics, enabling adaptation to network changes without complete graph recalculation. Experimental validation on controlled graphs and the Kyoto dataset demonstrates competitive performance, achieving 93% accuracy against brute-force methods and showing 69.1% exact matches with Dijkstra’s algorithm on real-world topologies. The framework provides a simulation-ready environment for vulnerability assessment and defensive planning in dynamic networks.https://ieeexplore.ieee.org/document/11131165/Cyber attack simulationgraph neural networkreinforcement learning |
| spellingShingle | Min Geun Song Jaewoong Choi Huy Kang Kim GAT-APG: Graph Attention Network-Based Attack Path Generation for Security Simulation IEEE Access Cyber attack simulation graph neural network reinforcement learning |
| title | GAT-APG: Graph Attention Network-Based Attack Path Generation for Security Simulation |
| title_full | GAT-APG: Graph Attention Network-Based Attack Path Generation for Security Simulation |
| title_fullStr | GAT-APG: Graph Attention Network-Based Attack Path Generation for Security Simulation |
| title_full_unstemmed | GAT-APG: Graph Attention Network-Based Attack Path Generation for Security Simulation |
| title_short | GAT-APG: Graph Attention Network-Based Attack Path Generation for Security Simulation |
| title_sort | gat apg graph attention network based attack path generation for security simulation |
| topic | Cyber attack simulation graph neural network reinforcement learning |
| url | https://ieeexplore.ieee.org/document/11131165/ |
| work_keys_str_mv | AT mingeunsong gatapggraphattentionnetworkbasedattackpathgenerationforsecuritysimulation AT jaewoongchoi gatapggraphattentionnetworkbasedattackpathgenerationforsecuritysimulation AT huykangkim gatapggraphattentionnetworkbasedattackpathgenerationforsecuritysimulation |