TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929495567499264 |
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| author | Lv, Mingqi Gao, HongZhe Qiu, Xuebo Chen, Tieming Zhu, Tiantian Chen, Jinyin Ji, Shouling |
| author_facet | Lv, Mingqi Gao, HongZhe Qiu, Xuebo Chen, Tieming Zhu, Tiantian Chen, Jinyin Ji, Shouling |
| contents | APT (Advanced Persistent Threat) with the characteristics of persistence, stealth, and diversity is one of the greatest threats against cyber-infrastructure. As a countermeasure, existing studies leverage provenance graphs to capture the complex relations between system entities in a host for effective APT detection. In addition to detecting single attack events as most existing work does, understanding the tactics / techniques (e.g., Kill-Chain, ATT&CK) applied to organize and accomplish the APT attack campaign is more important for security operations. Existing studies try to manually design a set of rules to map low-level system events to high-level APT tactics / techniques. However, the rule based methods are coarse-grained and lack generalization ability, thus they can only recognize APT tactics and cannot identify fine-grained APT techniques and mutant APT attacks. In this paper, we propose TREC, the first attempt to recognize APT tactics / techniques from provenance graphs by exploiting deep learning techniques. To address the "needle in a haystack" problem, TREC segments small and compact subgraphs covering individual APT technique instances from a large provenance graph based on a malicious node detection model and a subgraph sampling algorithm. To address the "training sample scarcity" problem, TREC trains the APT tactic / technique recognition model in a few-shot learning manner by adopting a Siamese neural network. We evaluate TREC based on a customized dataset collected and made public by our team. The experiment results show that TREC significantly outperforms state-of-the-art systems in APT tactic recognition and TREC can also effectively identify APT techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15147 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning Lv, Mingqi Gao, HongZhe Qiu, Xuebo Chen, Tieming Zhu, Tiantian Chen, Jinyin Ji, Shouling Cryptography and Security Machine Learning APT (Advanced Persistent Threat) with the characteristics of persistence, stealth, and diversity is one of the greatest threats against cyber-infrastructure. As a countermeasure, existing studies leverage provenance graphs to capture the complex relations between system entities in a host for effective APT detection. In addition to detecting single attack events as most existing work does, understanding the tactics / techniques (e.g., Kill-Chain, ATT&CK) applied to organize and accomplish the APT attack campaign is more important for security operations. Existing studies try to manually design a set of rules to map low-level system events to high-level APT tactics / techniques. However, the rule based methods are coarse-grained and lack generalization ability, thus they can only recognize APT tactics and cannot identify fine-grained APT techniques and mutant APT attacks. In this paper, we propose TREC, the first attempt to recognize APT tactics / techniques from provenance graphs by exploiting deep learning techniques. To address the "needle in a haystack" problem, TREC segments small and compact subgraphs covering individual APT technique instances from a large provenance graph based on a malicious node detection model and a subgraph sampling algorithm. To address the "training sample scarcity" problem, TREC trains the APT tactic / technique recognition model in a few-shot learning manner by adopting a Siamese neural network. We evaluate TREC based on a customized dataset collected and made public by our team. The experiment results show that TREC significantly outperforms state-of-the-art systems in APT tactic recognition and TREC can also effectively identify APT techniques. |
| title | TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2402.15147 |