Context-Aware Attended-over Distributed Specificity for Information Extraction in Cybersecurity

Cybersecurity can benefit substantially from a timely discovery of cyber threats in part taking advantage of information extraction techniques for capturing vulnerabilities that are communicated via textual channels. One of the latest techniques was attended-over distributed specificity (ADS) for te...

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Bibliographic Details
Main Authors: Prayogo, Nicholas, Amjadian, Ehsan, McDonnell, Serena, Abid, Muhammad Rizwan
Format: Conference Proceeding
Language:English
Subjects:
Computer architecture
Cybersecurity
Feature extraction
Gold
Information retrieval
Machine Learning
Mobile communication
Natural Language Processing
Natural languages
Term Extraction
Training
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Summary:Cybersecurity can benefit substantially from a timely discovery of cyber threats in part taking advantage of information extraction techniques for capturing vulnerabilities that are communicated via textual channels. One of the latest techniques was attended-over distributed specificity (ADS) for term extraction, which was tested on a cybersecurity dataset for this use case called Cyber ATE. However, in the absence of provisioning context in the dataset, the detection of terms could still occasionally fail. Thus, in our present work, we supplement CyberATE by including context phrases surrounding term candidates with varying window sizes. Then, we designed and evaluated new architectures for context-aware ATE. Our new architectures considerably beat the previous state-of-the-art, by as much as 12.48% F1-score. Further, we also show that providing context helps in attaining better classification, especially in improving recall, reaching as high as 5 % improvement. This is highly important especially in discovering new vulnerabilities in cybersecurity, since false negatives could lead to undiscovered vulnerabilities. Efficient and robust discovery of critical information pertaining to new cyber threats can facilitate and speedup the process to address vulnerabilities, well before any cyberattacks aim the target system or operation.
ISSN:2644-3163
DOI:10.1109/IEMCON56893.2022.9946567