Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning
Pablo Loyola, Kugamoorthy Gajananan, Yuji Watanabe, Fumiko Satoh
Abstract
In this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports. The goal is to explore if natural language processing methods can provide relevant and actionable knowledge to contribute to better understand malicious behavior. Our method consists of an attention-based Bi-LSTM which achieved competitive performance of 0.57 for the Subtask 1. In the due process we also present ablation studies across multiple embeddings and their level of representation and also report the strategies we used to mitigate the extreme imbalance between classes.- Anthology ID:
- S18-1143
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 885–889
- Language:
- URL:
- https://aclanthology.org/S18-1143
- DOI:
- 10.18653/v1/S18-1143
- Cite (ACL):
- Pablo Loyola, Kugamoorthy Gajananan, Yuji Watanabe, and Fumiko Satoh. 2018. Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 885–889, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning (Loyola et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/S18-1143.pdf