Cyber Information Retrieval Through Pragmatics Understanding and Visualization

The amount of cybersecurity-related information is extraordinarily increasing, given the fast-growing number cybersecurity attacks and significant influence brought by them. How to efficiently obtain precisely understand relevant knowledge in sea on becomes a challenge. In this article, we propose an innovative retrieval scheme that supports automatic indexing searching based semantic contents hidden metadata. proposed leverages customized neural model incorporates new linguistic features word embedding identifying entities related incidents from text. We implement novel search engine demonstrate effective, understandable pragmatic schema. Comprehensive performance evaluation over real-world datasets has been conducted validate algorithms techniques developed for retrieval. makes it possible conduct augmented search, analytics, visualization, with ultimate goal providing direct efficient results help people truly information.