scholarly journals CASIE: Extracting Cybersecurity Event Information from Text

2020 ◽  
Vol 34 (05) ◽  
pp. 8749-8757 ◽  
Author(s):  
Taneeya Satyapanich ◽  
Francis Ferraro ◽  
Tim Finin
Keyword(s):  
Neural Networks ◽  
Event Detection ◽  
Deep Neural Networks ◽  
Knowledge Graph ◽  
Semantic Model ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Semantic Roles ◽  
Event Information ◽  
Event Based

We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 that are labeled with rich, event-based annotations and that covers both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types (e.g., file, device, software, money). CASIE uses different deep neural networks approaches with attention and can incorporate rich linguistic features and word embeddings. We have conducted experiments on each component in the event detection pipeline and the results show that each subsystem performs well.

scholarly journals Word-embeddings Italian semantic spaces: A semantic model for psycholinguistic research

Psihologija ◽  
2017 ◽  
Vol 50 (4) ◽  
pp. 503-520 ◽  
Author(s):  
Marco Marelli
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Semantic Space ◽  
Distributional Semantics ◽  
Semantic Model ◽  
Word Embeddings ◽  
Psycholinguistic Research ◽  
Graphic Interface ◽  
Test Sets ◽  
Semantic Spaces

Distributional semantics has been for long a source of successful models in psycholinguistics, permitting to obtain semantic estimates for a large number of words in an automatic and fast way. However, resources in this respect remain scarce or limitedly accessible for languages different from English. The present paper describes WEISS (Word-Embeddings Italian Semantic Space), a distributional semantic model based on Italian. WEISS includes models of semantic representations that are trained adopting state-of-the-art word-embeddings methods, applying neural networks to induce distributed representations for lexical meanings. The resource is evaluated against two test sets, demonstrating that WEISS obtains a better performance with respect to a baseline encoding word associations. Moreover, an extensive qualitative analysis of the WEISS output provides examples of the model potentialities in capturing several semantic phenomena. Two variants of WEISS are released and made easily accessible via web through the SNAUT graphic interface.

Device Invariant Deep Neural Networks for Pulmonary Audio Event Detection Across Mobile and Wearable Devices

2021 ◽  
Author(s):  
Mohsin Y Ahmed ◽  
Li Zhu ◽  
Md Mahbubur Rahman ◽  
Tousif Ahmed ◽  
Jilong Kuang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Event Detection ◽  
Deep Neural Networks ◽  
Wearable Devices ◽  
Audio Event

Denoising of event-based sensors with deep neural networks

2021 ◽  
Author(s):  
Zhihong Zhang ◽  
Jinli Suo ◽  
Qionghai Dai
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Event Based
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