  We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA
is a detection and classification algorithm based on the Machine Learning
technique (Random Forest). The goal of this paper is to show the power of SIDRA
for quick and accurate signal detection and classification. We first diagnose
the power of the method with simulated light curves and try it on a subset of
the Kepler space mission catalogue. We use five classes of simulated light
curves (CONSTANT, TRANSIT, VARIABLE, MLENS and EB for constant light curves,
transiting exoplanet, variable, microlensing events and eclipsing binaries,
respectively) to analyse the power of the method. The algorithm uses four
features in order to classify the light curves. The training sample contains
5000 light curves (1000 from each class) and 50000 random light curves for
testing. The total SIDRA success ratio is $\geq 90\%$. Furthermore, the success
ratio reaches 95 - 100$\%$ for the CONSTANT, VARIABLE, EB, and MLENS classes
and 92$\%$ for the TRANSIT class with a decision probability of 60$\%$. Because
the TRANSIT class is the one which fails the most, we run a simultaneous fit
using SIDRA and a Box Least Square (BLS) based algorithm for searching for
transiting exoplanets. As a result, our algorithm detects 7.5$\%$ more planets
than a classic BLS algorithm, with better results for lower signal-to-noise
light curves. SIDRA succeeds to catch 98$\%$ of the planet candidates in the
Kepler sample and fails for 7$\%$ of the false alarms subset. SIDRA promises to
be useful for developing a detection algorithm and/or classifier for large
photometric surveys such as TESS and PLATO exoplanet future space missions.
