KillChainGraph: ML Framework for Predicting and Mapping ATT&CK Techniques
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908502271721472 |
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| author | Singh, Chitraksh Dhanraj, Monisha Huang, Ken |
| author_facet | Singh, Chitraksh Dhanraj, Monisha Huang, Ken |
| contents | The escalating complexity and volume of cyberattacks demand proactive detection strategies that go beyond traditional rule-based systems. This paper presents a phase-aware, multi-model machine learning framework that emulates adversarial behavior across the seven phases of the Cyber Kill Chain using the MITRE ATT&CK Enterprise dataset. Techniques are semantically mapped to phases via ATTACK-BERT, producing seven phase-specific datasets. We evaluate LightGBM, a custom Transformer encoder, fine-tuned BERT, and a Graph Neural Network (GNN), integrating their outputs through a weighted soft voting ensemble. Inter-phase dependencies are modeled using directed graphs to capture attacker movement from reconnaissance to objectives. The ensemble consistently achieved the highest scores, with F1-scores ranging from 97.47% to 99.83%, surpassing GNN performance (97.36% to 99.81%) by 0.03%--0.20% across phases. This graph-driven, ensemble-based approach enables interpretable attack path forecasting and strengthens proactive cyber defense. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KillChainGraph: ML Framework for Predicting and Mapping ATT&CK Techniques Singh, Chitraksh Dhanraj, Monisha Huang, Ken Cryptography and Security Artificial Intelligence The escalating complexity and volume of cyberattacks demand proactive detection strategies that go beyond traditional rule-based systems. This paper presents a phase-aware, multi-model machine learning framework that emulates adversarial behavior across the seven phases of the Cyber Kill Chain using the MITRE ATT&CK Enterprise dataset. Techniques are semantically mapped to phases via ATTACK-BERT, producing seven phase-specific datasets. We evaluate LightGBM, a custom Transformer encoder, fine-tuned BERT, and a Graph Neural Network (GNN), integrating their outputs through a weighted soft voting ensemble. Inter-phase dependencies are modeled using directed graphs to capture attacker movement from reconnaissance to objectives. The ensemble consistently achieved the highest scores, with F1-scores ranging from 97.47% to 99.83%, surpassing GNN performance (97.36% to 99.81%) by 0.03%--0.20% across phases. This graph-driven, ensemble-based approach enables interpretable attack path forecasting and strengthens proactive cyber defense. |
| title | KillChainGraph: ML Framework for Predicting and Mapping ATT&CK Techniques |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2508.18230 |