Unstructured Big Data Threat Intelligence Parallel Mining Algorithm

Abstract

To efficiently mine threat intelligence from the vast array of open-source cybersecurity analysis reports on the web, we have developed the Parallel Deep Forest-based Multi-Label Classification (PDFMLC) algorithm. Initially, open-source cybersecurity analysis reports are collected and converted into a standardized text format. Subsequently, five tactics category labels are annotated, creating a multi-label dataset for tactics classification. Addressing the limitations of low execution efficiency and scalability in the sequential deep forest algorithm, our PDFMLC algorithm employs broadcast variables and the Lempel-Ziv-Welch (LZW) algorithm, significantly enhancing its acceleration ratio. Furthermore, our proposed PDFMLC algorithm incorporates label mutual information from the established dataset as input features. This captures latent label associations, significantly improving classification accuracy. Finally, we present the PDFMLC-based Threat Intelligence Mining (PDFMLC-TIM) method. Experimental results demonstrate that the PDFMLC algorithm exhibits exceptional node scalability and execution efficiency. Simultaneously, the PDFMLC-TIM method proficiently conducts text classification on cybersecurity analysis reports, extracting tactics entities to construct comprehensive threat intelligence. As a result, successfully formatted STIX2.1 threat intelligence is established.

Keywords

• unstructured big data mining
• parallel deep forest
• multi-label classification algorithm
• threat intelligence