Online Transition-Based Feature Generation for Anomaly Detection in
  Concurrent Data Streams
Yinzheng Zhong Alexei Lisitsa
In this paper, we introduce the transition-based feature generator (TFGen)
technique, which reads general activity data with attributes and generates
step-by-step generated data. The activity data may consist of network activity
from packets, system calls from processes or classified activity from
surveillance cameras. TFGen processes data online and will generate data with
encoded historical data for each incoming activity with high computational
efficiency. The input activities may concurrently originate from distinct
traces or channels. The technique aims to address issues such as
domain-independent applicability, the ability to discover global process
structures, the encoding of time-series data, and online processing capability.