Feature Driven Learning Framework for Cybersecurity Event Detection

Published in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019

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 cybersecurity 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 cybersecurity incidents.

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Recommended citation:

@inproceedings{ji2019feature,
    title={Feature Driven Learning Framework for Cybersecurity Event Detection},
    author={Ji, Taoran and Zhang, Xuchao and Self, Nathan and Fu, Kaiqun and Lu, Chang-Tien and Ramakrishnan, Naren},
    booktitle={2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
    pages={634--641},
    year={2019},
    organization={IEEE}
}