Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media

摘要: Social media is often viewed as a sensor into various societal events such disease outbreaks, protests, and elections. We describe the use of social crowdsourced to gain insight ongoing cyber-attacks. Our approach detects broad range cyber-attacks (e.g., distributed denial service (DDoS) attacks, data breaches, account hijacking) in weakly supervised manner using just small set seed event triggers requires no training or labeled samples. A new query expansion strategy based on convolution kernels dependency parses helps model semantic structure aids identifying key characteristics. Through large-scale analysis over Twitter, we demonstrate that our consistently identifies encodes events, outperforming existing methods.