  Time-varying phenomena are one of the most substantial sources of
astrophysical information and their study has led to many fundamental
discoveries in modern astronomy. We have developed an automated tool to search
and analyze variable sources in the near infrared $\rm K_{s}$-band, using the
data from the Vista Variables in the V\'ia L\'actea (VVV) ESO Public Large
Survey. This process relies on the characterization of variable sources using
different variability indices, calculated from time series generated with Point
Spread Function photometry of sources under analysis. In particular, we used
two main indices: the total amplitude $\rm \Delta K_s$ and the eta index,
$\eta$, to identify variable sources. Once variable objects are identified,
periods are determined with Generalized Lomb-Scargle periodograms, and the
Information Potential Metric. Variability classes are assigned according to a
compromise between comparisons with VVV Templates and the period of the
variability. The automated tool is applied on VVV tiles d001 and d002 and led
to discovery of 200 variable sources. We detected 70 irregular variable sources
and 130 periodic ones. In addition nine open cluster candidates projected in
the region are analyzed, the infrared variable candidates found around these
clusters are further scrutinized by cross-matching their locations against
emission star candidates from VPHAS+ survey $\rm H_{\alpha}$ color cuts.
