TY - GEN
T1 - Crowdsourcing cybersecurity
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
AU - Khandpur, Rupinder Paul
AU - Ji, Taoran
AU - Jan, Steve
AU - Wang, Gang
AU - Lu, Chang Tien
AU - Ramakrishnan, Naren
N1 - Funding Information:
The authors gratefully acknowledge the funding support for this research from National Science Foundation (under award numbers CNS-1717028, DGE-1545362, IIS-1633363) and Northrop Grumman (NGC 7500144847).
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - 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 a weakly supervised manner using just a small set of seed event triggers and requires no training or labeled samples. A new query expansion strategy based on convolution kernels and dependency parses helps model semantic 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.
AB - 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 a weakly supervised manner using just a small set of seed event triggers and requires no training or labeled samples. A new query expansion strategy based on convolution kernels and dependency parses helps model semantic 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.
KW - Cyber attacks
KW - Cyber security
KW - Dynamic query expansion
KW - Event detection
KW - Social media
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85037356582&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037356582&partnerID=8YFLogxK
U2 - 10.1145/3132847.3132866
DO - 10.1145/3132847.3132866
M3 - Conference contribution
AN - SCOPUS:85037356582
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1049
EP - 1057
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery,
Y2 - 6 November 2017 through 10 November 2017
ER -