CSER: Enhancing Cybersecurity Entity Recognition Through Multidimensional Feature Fusion

In the rapidly evolving landscape of cybersecurity, the accurate identification and classification of cybersecurity-related entities within textual data have gained paramount importance. This paper presents a novel Cybersecurity Entity Recognition (CSER) model that addresses the distinct challenges posed by the cybersecurity domain. Unlike conventional Named Entity Recognition (NER) methods, the cybersecurity context introduces domain-specific features, such as specialized patterns, keywords, and linguistic structures. Moreover, the presence of out-of-vocabulary words and domain-specific symbols further complicates entity recognition. To tackle these challenges, our CSER model integrates contextual, semantic, and morphological features, leveraging recurrent neural networks, convolutional neural networks, and conditional random fields. We investigate various embeddings, model architectures, and configurations, analyzing their influence on benchmark performance metrics. The outcomes of our experiments demonstrate a consistent superiority of our proposed CSER models over existing ones in terms of diverse performance metrics. This underscores the efficacy of our models in precisely recognizing cybersecurity-related entities. This research contributes to the advancement of entity recognition in the cybersecurity domain, shedding light on the pivotal role of domain-specific features and sophisticated architectures in enhancing accuracy and reliability.