Darknet Public Hazard Entity Recognition Based on Deep Learning
Panpan Zhang, Xuebin Wang, Jing Ya, Jiapeng Zhao, Tingwen Liu, Jinqiao Shi · 2021
Due to the strong protection of anonymity, Darknet has been exploited by criminals to distribute harmful content and banned items, such as drugs, weapons, and malware, which are regarded as public hazard entities. The task of public hazard entity recognition can help to detect and analyze malicious activities in the Darknet. This paper focuses on the research of Chinese public hazard entity recognition in the field of illegal drugs. To evade detection and surveillance, Chinese public hazard entities in the Darknet usually utilize disguised forms, like homophones and multi-entities in a sentence, which makes it harder to identify them using traditional entity recognition methods. In this paper, we present an effective deep learning-based multi-information fusion model to identify Chinese public hazard entities in the Darknet. Specifically, we introduce the grammatical information by adding Pinyin and lexical features, and strengthen the semantic features by adding the word vectors from one advanced pre-trained language representation model. Then we combine these three parts with a classical sequence annotation architecture used in general named entity recognition to form our ultimate model. At last we construct a real dataset from drugs-related groups in the Darknet and conduct several experiments to evaluate our model. The experimental result verifies that our proposed model gains a good performance on the recognition of Darknet public hazard entities.