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A Cyber Threat Entity Recognition Method Based on Robust Feature Representation and Adversarial Training

Published: 28 February 2024 Publication History

Abstract

With the development of Internet, cybersecurity attracts people's attention. In order to better protect cybersecurity, we can comprehensively analyze the security events based on cyber threat intelligence. We aim to identify correlations between security events to proactively address potential threats. However, there are still many challenges when people use cyber threat intelligence. Cyber threat intelligence mainly exists in unstructured form. It is necessary to extract the important elements from it. We design a cyber threat entity recognition method to help the analysis of cyber threat intelligence. The formation of accurate and robust feature representation is the key to realize the task of cyber threat entity recognition, but the feature representation of text is susceptible to noise interference. In order to form an accurate representation of the text, we design a robust feature representation method which extracts features based on multiple perspectives and adopts a mutual learning mechanism to promote feature interaction. It adopts iterative fusion to form the final feature representation. And we use an adversarial training framework that can learn attack strategies to alleviate the problem of noise interference. We conduct relevant experiments on the cyber threat intelligence dataset DNRTI. The experimental results show that our method can be used in cyber threat intelligence analysis.

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  1. A Cyber Threat Entity Recognition Method Based on Robust Feature Representation and Adversarial Training

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      ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
      October 2023
      589 pages
      ISBN:9798400707988
      DOI:10.1145/3633637
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 28 February 2024

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      Author Tags

      1. Adversarial training
      2. Cyber threat intelligence
      3. Feature representation
      4. Threat entity recognition

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