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The pervasive reach of social media like the X platform, formerly known as Twitter, offers unique opportunities for real-time analysis of cyberattack developments. By parsing and classifying tweets related to cyberattacks, we can glean valuable insights into their type, location, impact, and potential mitigation strategies. However, with millions of daily tweets, manual analysis is inefficient and time-consuming. This paper proposes an interactive and automated dashboard powered by natural language processing to effectively address this challenge. First, we created the CybAttT dataset, which contains 36,071 manually labeled English cyberattack tweets. We experimented with different classification algorithms. Following that, the best model was deployed and integrated into the streaming pipeline for real-time classification. This dynamic dashboard makes use of four different visualization formats: a geographical map, a data table, informative tiles, and a bar chart. Users can readily access crucial information about attacks, including location, timing, and perpetrators, enabling a swift response and mitigation efforts. Our experimental results demonstrated the dashboard’s promising visualization capabilities, highlighting its potential as a valuable tool for organizations and individuals seeking an intuitive and comprehensive overview of cyberattack events. |
