A Study on Security Trend based on News Analysis
| Autor: | Chia-Mei Chen, Yi-Hung Liu, Gu-Hsin Lai, Jun-Jie Fang, Dan-Wei Marian Wen |
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| Rok vydání: | 2019 |
| Předmět: |
Data source
Topic model ComputingMilieux_THECOMPUTINGPROFESSION business.industry Internet privacy ComputingMilieux_LEGALASPECTSOFCOMPUTING Workload 02 engineering and technology Read through Order (exchange) 020204 information systems 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing The Internet News analytics business |
| Zdroj: | iCAST |
| DOI: | 10.1109/icawst.2019.8923373 |
| Popis: | Workload of cybersecurity administrators has significantly increased with the proliferation of the internet and the accompanied cyberattacks. In order to help firms to identify most recent and emerging cyberattacks in a timely manner, this research applies machine learning methods to detect cybersecurity trends. As the rich, multifaceted, and updated online cybersecurity news serve as key information sources for cybersecurity administrators, this research utilizes the wealth of online cybersecurity news as the data source and develops a system to automatically collect multiple online cybersecurity news outlets, analyze collected news to detect emergence of cybersecurity events and present trend of cybersecurity news. This research can facilitate cybersecurity administrators in saving their time to read through multiple cybersecurity news websites and organize events from their memories or other records, thus enhance firms’ capacity to actively protect against potential cyberattacks. |
| Databáze: | OpenAIRE |
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