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Advanced persistent threats (APTs) have novel features such as multi-stagepenetration, highly-tailored intention, and evasive tactics. APTs defenserequires fusing multi-dimensional Cyber threat intelligence data to identifyattack intentions and conducts efficient knowledge discovery strategies bydata-driven machine learning to recognize entity relationships. However,data-driven machine learning lacks generalization ability on fresh or unknownsamples, reducing the accuracy and practicality of the defense model. Besides,the private deployment of these APT defense models on heterogeneousenvironments and various network devices requires significant investment incontext awareness (such as known attack entities, continuous network states,and current security strategies). In this paper, we propose a few-shotmulti-domain knowledge rearming (FMKR) scheme for context-aware defense againstAPTs. By completing multiple small tasks that are generated from differentnetwork domains with meta-learning, the FMKR firstly trains a model with gooddiscrimination and generalization ability for fresh and unknown APT attacks. Ineach FMKR task, both threat intelligence and local entities are fused into thesupport/query sets in meta-learning to identify possible attack stages.Secondly, to rearm current security strategies, an finetuning-based deploymentmechanism is proposed to transfer learned knowledge into the student model,while minimizing the defense cost. Compared to multiple model replacementstrategies, the FMKR provides a faster response to attack behaviors whileconsuming less scheduling cost. Based on the feedback from multiple real usersof the Industrial Internet of Things (IIoT) over 2 months, we demonstrate thatthe proposed scheme can improve the defense satisfaction rate.

Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats