Threat Intelligence Named Entity Recognition with Fusion Residual Perception Network

ZENG Wen-li; CHEN Ji-xin

Computer & Telecommunication ›› 2025, Vol. 1 ›› Issue (4) : 30-37.

ZENG Wen-li^1,2, CHEN Ji-xin¹

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Abstract

Addressing the challenges faced by general domain named entity recognition methods, which struggle to identify specialized terms and security entities within the cybersecurity domain, and suffer from insufficient feature extraction leading to low accuracy in cybersecurity entity recognition, this paper introduces a new model named Res-Inception Bi-LSTM-CRF (RIBIC). The RIBIC model leverages a Res-Inception Network to extract multi-granularity features, thereby capturing a richer set of feature information. Furthermore, an in-house cybersecurity domain-specific dictionary is developed, and a dictionary-based matching correction algorithm is incorporated to enhance the precision of entity recognition. The experimental results indicate that on two publicly available threat intelligence datasets, the F1 scores achieved are 94.09% and 83.91%, representing improvements of 15.02% and 15.72% over the baseline models, respectively. These findings robustly validate the effectiveness of the proposed method for named entity recognition in the threat intelligence domain.

Key words

Cyber Threat Intelligence / Named Entity Recognition / Res-Inception Network

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ZENG Wen-li, CHEN Ji-xin. Threat Intelligence Named Entity Recognition with Fusion Residual Perception Network[J]. Computer & Telecommunication. 2025, 1(4): 30-37

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