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arXiv · 1702.07745

Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media

Rupinder Paul Khandpur·Taoran Ji·Steve Jan·Gang Wang·Chang-Tien Lu·Naren Ramakrishnan
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Abstract

Social media is often viewed as a sensor into various societal events such as disease outbreaks, protests, and elections. We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our approach detects a broad range of cyber-attacks (e.g., distributed denial of service (DDOS) attacks, data breaches, and account hijacking) in an unsupervised manner using just a limited fixed set of seed event triggers. A new query expansion strategy based on convolutional kernels and dependency parses helps model reporting structure and aids in identifying key event characteristics. Through a large-scale analysis over Twitter, we demonstrate that our approach consistently identifies and encodes events, outperforming existing methods.

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Rupinder Paul Khandpur, Taoran Ji, Steve Jan, Gang Wang, Chang-Tien Lu, Naren Ramakrishnan. 2017-02-24. Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media. https://doi.org/10.1145/3132847.3132866

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