  The real-time recognition of neutrino signals from astrophysical objects with
very-low false alarm rate and short-latency, is crucial to perform
multi-messenger detection, especially in the case of distant core-collapse
supernovae accessible with the next generation of large-scale neutrino
telescopes. The current time-based selection algorithms implemented in
operating online monitors depend mainly on the number of events (multiplicity)
detected in a fixed time window, under the hypothesis of Poisson-distributed
background. However, these methods are not capable of exploiting the time
profile discrepancies between the expected supernova neutrino burst and the
stationary background.
  In this paper we propose a new general and flexible technique (beta filter
method) which provides specific decision boundaries on the cluster
multiplicity-duration plane, guaranteeing the desired false alarm rate in an
analytical way. The performance is evaluated using the injection of a general
purpose SN-like signal on top of realistic background rates in current
detectors. An absolute gain in efficiency of up to $\sim 80\%$ is achieved
compared with the standard techniques, and a new ultra-low multiplicity region
is unveiled.
