  The primary goal of online change detection (OCD) is to promptly identify
changes in the data stream. OCD problem find a wide variety of applications in
diverse areas, e.g., security detection in smart grids and intrusion detection
in communication networks. Prior research usually assumes precise knowledge of
the system parameters. Nevertheless, this presumption often proves unattainable
in practical scenarios due to factors such as estimation errors, system
updates, etc. This paper aims to take the first attempt to develop a
triadic-OCD framework with certifiable robustness, provable optimality, and
guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be
realized in a fully asynchronous distributed manner, easing the necessity of
transmitting the data to a single server. This asynchronous mechanism could
also mitigate the straggler issue that faced by traditional synchronous
algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is
theoretically analyzed, and its iteration complexity to achieve an
$\epsilon$-optimal point is derived. Extensive experiments have been conducted
to elucidate the effectiveness of the proposed method.
