  Network traffic analytics technology is a cornerstone for cyber security
systems. We demonstrate its use through three popular and contemporary cyber
security applications in intrusion detection, malware analysis and botnet
detection. However, automated traffic analytics faces the challenges raised by
big traffic data. In terms of big data's three characteristics --- volume,
variety and velocity, we review three state of the art techniques to mitigate
the key challenges including real-time traffic classification, unknown traffic
classification, and efficiency of classifiers. The new techniques using
statistical features, unknown discovery and correlation analytics show
promising potentials to deal with big traffic data. Readers are encouraged to
devote to improving the performance and practicability of automatic traffic
analytic in cyber security.
