  Although there have been many solutions applied, the safety challenges
related to the password security mechanism are not reduced. The reason for this
is that while the means and tools to support password attacks are becoming more
and more abundant, the number of transaction systems through the Internet is
increasing, and new services systems appear. For example, IoT also uses
password-based authentication. In this context, consolidating password-based
authentication mechanisms is critical, but monitoring measures for timely
detection of attacks also play an important role in this battle. The password
attack detection solutions being used need to be supplemented and improved to
meet the new situation. In this paper we propose a solution that automatically
detects online password attacks in a way that is based solely on the network,
using unsupervised learning techniques and protected application orientation.
Our solution, therefore, minimizes dependence on the factors encountered by
host-based or supervised learning solutions. The certainty of the solution
comes from using the results of an in-depth analysis of attack characteristics
to build the detection capacity of the mechanism. The solution was implemented
experimentally on the real system and gave positive results.
