Open AccessThreat Action Extraction using Information Retrieval
Author(s) -
Chia-Mei Chen,
Jing-Yun Kan,
Ya-Hui Ou,
Zheng-Xun Cai,
Albert Guan
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.110702
Subject(s) - computer science , preprocessor , hacker , action (physics) , data mining , data pre processing , dimension (graph theory) , feature extraction , information retrieval , ontology , precision and recall , artificial intelligence , computer security , philosophy , physics , mathematics , epistemology , quantum mechanics , pure mathematics
To gain insight into potential cyber threats, this research proposes a novel automatic threat action retrieval system, which collects and analyzes various data sources including security news, incident analysis reports, and darknet hacker forums and develops an improved data preprocessing method to reduce feature dimension and a novel query match algorithm to capture effective threat actions automatically without manually predefined ontology applied by the past research. The experimental results illustrate that The proposed method achieves an accuracy of 94.7% and a recall rate of 95.8% and outperforms the previous research. The proposed solution can extract effective threat actions automatically and efficiently.