Name
Abstract | ||
|---|---|---|
Cybersecurity event detection is a crucial problem for mitigating effects on various aspects of society. Social media has become a notable source of indicators for detection of diverse events. Though previous social media based strategies for cyber-security event detection focus on mining certain event-related words, the dynamic and evolving nature of online discourse limits the performance of these approaches. Further, because these are typically unsupervised or weakly supervised learning strategies, they do not perform well in an environment of biased samples, noisy context, and informal language which is routine for online, user-generated content. This paper takes a supervised learning approach by proposing a novel multi-task learning based model. Our model can handle diverse structures in feature space by learning models for different types of potential high-profile targets simultaneously. For parameter optimization, we develop an efficient algorithm based on the alternating direction method of multipliers. Through extensive experiments on a real world Twitter dataset, we demonstrate that our approach consistently outperforms existing methods at encoding and identifying cyber-security incidents.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341161.3342871 | ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining
Vancouver
British Columbia
Canada
August, 2019 |
Keywords | Field | DocType |
cybersecurity event detection,diverse events,event-related words,dynamic nature,learning strategies,online user-generated content,supervised learning approach,multitask learning based model,social media based strategies,feature driven learning framework | Computer science,Artificial intelligence,Machine learning | Conference |
ISSN | ISBN | Citations |
2473-9928 | 978-1-4503-6868-1 | 0 |
PageRank | References | Authors |
0.34 | 25 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taoran Ji | 1 | 17 | 3.39 |
| Xuchao Zhang | 2 | 37 | 12.65 |
| Nathan Self | 3 | 101 | 9.65 |
| Kaiqun Fu | 4 | 21 | 5.24 |
| Chang-Tien Lu | 5 | 1097 | 115.77 |
| Naren Ramakrishnan | 6 | 1913 | 176.25 |