SecTracer: A Framework for Uncovering the Root Causes of Network Intrusions via Security Provenance
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909899431084032 |
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| author | Lee, Seunghyeon Seo, Hyunmin Heo, Hwanjo Wang, Anduo Shin, Seungwon Kim, Jinwoo |
| author_facet | Lee, Seunghyeon Seo, Hyunmin Heo, Hwanjo Wang, Anduo Shin, Seungwon Kim, Jinwoo |
| contents | Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophisticated attacks such as advanced persistent threats (APTs), which often exploit multiple vectors. To address this challenge, we introduce the concept of network-level security provenance, which enables the systematic establishment of causal relationships across hosts at the network level, facilitating the accurate identification of the root causes of security incidents. Building on this concept, we present SecTracer as a framework for a network-wide provenance analysis. SecTracer offers three main contributions: (i) comprehensive and efficient forensic data collection in enterprise networks via software-defined networking (SDN), (ii) reconstruction of attack histories through provenance graphs to provide a clear and interpretable view of intrusions, and (iii) proactive attack prediction using probabilistic models. We evaluated the effectiveness and efficiency of SecTracer through a real-world APT simulation, demonstrating its capability to enhance threat mitigation while introducing less than 1% network throughput overhead and negligible latency impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09266 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SecTracer: A Framework for Uncovering the Root Causes of Network Intrusions via Security Provenance Lee, Seunghyeon Seo, Hyunmin Heo, Hwanjo Wang, Anduo Shin, Seungwon Kim, Jinwoo Cryptography and Security Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophisticated attacks such as advanced persistent threats (APTs), which often exploit multiple vectors. To address this challenge, we introduce the concept of network-level security provenance, which enables the systematic establishment of causal relationships across hosts at the network level, facilitating the accurate identification of the root causes of security incidents. Building on this concept, we present SecTracer as a framework for a network-wide provenance analysis. SecTracer offers three main contributions: (i) comprehensive and efficient forensic data collection in enterprise networks via software-defined networking (SDN), (ii) reconstruction of attack histories through provenance graphs to provide a clear and interpretable view of intrusions, and (iii) proactive attack prediction using probabilistic models. We evaluated the effectiveness and efficiency of SecTracer through a real-world APT simulation, demonstrating its capability to enhance threat mitigation while introducing less than 1% network throughput overhead and negligible latency impact. |
| title | SecTracer: A Framework for Uncovering the Root Causes of Network Intrusions via Security Provenance |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2511.09266 |