Feature Representation Models for Cyber Attack Event Extraction
| Autor: | Xiaoxin Lin, Likun Qiu, Xin Ying Qiu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Context model
business.industry Event (computing) Computer science Supervised learning Feature extraction 020206 networking & telecommunications 02 engineering and technology 010502 geochemistry & geophysics Machine learning computer.software_genre 01 natural sciences Feature model Data modeling Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Cyber-attack Artificial intelligence business Hidden Markov model computer 0105 earth and related environmental sciences |
| Zdroj: | WI Workshops |
| Popis: | We design and compare multiple feature representation models for classifying cyber attack events and their arguments. Experiment results show that combining lexical, contextual, and semantic features of a sentence performs well for identifying cyber attack event arguments with supervised learning methods and pre-annotated training and test data. However, with implementable simulation experiments with non-annotated test candidates, trigger-matching method works best for event type detection, while word-embedding feature model trained with large corpus performs much better than other models. The comparisons shed lights for future improvement on cyber attack news detection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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