Guangzhou University

Zhiwei Chang

Wenli Shang

Man Zhang

Zhong Cao

Zheng Zhou

Sha Huan

Bowen Wang

Hai Jie

Meng Zhang

Haotian Shi

Xiaobin Gao

Shuqi Li

Xiaojun Yang

A lightweight intrusion detection method includes obtaining a feature data set of the internet of vehicles and pre-processing data; dividing pre-processed data into an initial training set, an initial verification set and an initial test set according to a preset proportion, performing data balance on the initial training set to obtain a balanced training set, performing feature selection on the balanced training set, the initial verification set and the test set; obtaining a teacher model by training with the model training set and the model verification set; using the teacher model, the model training set and the model verification set for distillation training to obtain a student model; testing a size and complexity of the student model and the performance of the student model, and saving the student model that passes the test as a lightweight intrusion detection model; and deploying the lightweight intrusion detection model.

Lightweight intrusion detection method and system of internet of vehicles based on knowledge distillation

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

Growing community of inventors

Guangzhou, China

Zhiwei Chang