Guangzhou University

Sha Huan

Man Zhang

Zhong Cao

Wenli Shang

Hai Jie

Zhiwei Chang

Bowen Wang

Meng Zhang

Wenjing Zhao

Haotian Shi

Zhuo Chen

A network threat intelligence relational triple combined extraction method based on deep learning includes: (a) an entity and a relation are both extracted by using a combined extraction method, which solves the problem of a lack of interaction between entity and relation extraction tasks; (b) a problem of entity overlapping is solved by using a method based on a span; (c) a BERT large-scale pre-training model is used for vector representation of a text; and because the pre-training model includes contextual information learned from a large-scale corpus, semantic expression on a threat intelligence text by the model can be extremely enriched; and (d) various modal information, for example, time sequences, dependency relations, the spans, labels, and the like, is fused to enhance the interaction between multimodal information.

Network threat intelligence relational triple combined extraction method based on deep learning

A revocable lightweight group authentication method and system for an edge controller is described here. When the edge controller needs to be registered, an edge server generates a private key of the edge controller and sends the private key to the edge controller, and meanwhile adds the edge controller to a group list of the edge server; the edge server updates a certificate of the edge controller, adds the certificate to a certificate list of the edge server and sends the certificate to the edge controller so that the edge controller updates the private key according to the updated certificate; and then the edge controller generates a signature according to the updated private key, and sends the signature to the edge server so that the edge server authenticates the edge controller after determining that the signature meets preset requirements.

Revocable lightweight group authentication method and system for edge controller, and medium

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Guangzhou, China

Sha Huan