LMDG: Advancing Lateral Movement Detection Through High-Fidelity Dataset Generation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909721954353152 |
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| author | Mabrouk, Anas Hatem, Mohamed Mamun, Mohammad Saad, Sherif |
| author_facet | Mabrouk, Anas Hatem, Mohamed Mamun, Mohammad Saad, Sherif |
| contents | Lateral Movement (LM) attacks continue to pose a significant threat to enterprise security, enabling adversaries to stealthily compromise critical assets. However, the development and evaluation of LM detection systems are impeded by the absence of realistic, well-labeled datasets. To address this gap, we propose LMDG, a reproducible and extensible framework for generating high-fidelity LM datasets. LMDG automates benign activity generation, multi-stage attack execution, and comprehensive labeling of system and network logs, dramatically reducing manual effort and enabling scalable dataset creation. A central contribution of LMDG is Process Tree Labeling, a novel agent-based technique that traces all malicious activity back to its origin with high precision. Unlike prior methods such as Injection Timing or Behavioral Profiling, Process Tree Labeling enables accurate, step-wise labeling of malicious log entries, correlating each with a specific attack step and MITRE ATT\&CK TTPs. To our knowledge, this is the first approach to support fine-grained labeling of multi-step attacks, providing critical context for detection models such as attack path reconstruction. We used LMDG to generate a 25-day dataset within a 25-VM enterprise environment containing 22 user accounts. The dataset includes 944 GB of host and network logs and embeds 35 multi-stage LM attacks, with malicious events comprising less than 1% of total activity, reflecting a realistic benign-to-malicious ratio for evaluating detection systems. LMDG-generated datasets improve upon existing ones by offering diverse LM attacks, up-to-date attack patterns, longer attack timeframes, comprehensive data sources, realistic network architectures, and more accurate labeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LMDG: Advancing Lateral Movement Detection Through High-Fidelity Dataset Generation Mabrouk, Anas Hatem, Mohamed Mamun, Mohammad Saad, Sherif Cryptography and Security Lateral Movement (LM) attacks continue to pose a significant threat to enterprise security, enabling adversaries to stealthily compromise critical assets. However, the development and evaluation of LM detection systems are impeded by the absence of realistic, well-labeled datasets. To address this gap, we propose LMDG, a reproducible and extensible framework for generating high-fidelity LM datasets. LMDG automates benign activity generation, multi-stage attack execution, and comprehensive labeling of system and network logs, dramatically reducing manual effort and enabling scalable dataset creation. A central contribution of LMDG is Process Tree Labeling, a novel agent-based technique that traces all malicious activity back to its origin with high precision. Unlike prior methods such as Injection Timing or Behavioral Profiling, Process Tree Labeling enables accurate, step-wise labeling of malicious log entries, correlating each with a specific attack step and MITRE ATT\&CK TTPs. To our knowledge, this is the first approach to support fine-grained labeling of multi-step attacks, providing critical context for detection models such as attack path reconstruction. We used LMDG to generate a 25-day dataset within a 25-VM enterprise environment containing 22 user accounts. The dataset includes 944 GB of host and network logs and embeds 35 multi-stage LM attacks, with malicious events comprising less than 1% of total activity, reflecting a realistic benign-to-malicious ratio for evaluating detection systems. LMDG-generated datasets improve upon existing ones by offering diverse LM attacks, up-to-date attack patterns, longer attack timeframes, comprehensive data sources, realistic network architectures, and more accurate labeling. |
| title | LMDG: Advancing Lateral Movement Detection Through High-Fidelity Dataset Generation |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2508.02942 |