TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning


연구 분야: Safety



학회: CCS '24: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security


초록

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 also 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 have difficulty in identifying APT techniques. They also cannot adapt to mutant behaviors of existing APT tactics / techniques. 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.


Author Profile
Jinyin Chen

College of Information Engineering Zhejiang University of Technology Hangzhou China

China
Author Profile
Mingqi Lv

College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Andorra
Author Profile
Hongzhe Gao

College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Andorra

📄 논문 정보

발행 연도 2024년
인용수 7
출판 국가 Andorra, China
사이트 ACM
좋아요 수 0

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