Classification model challenge I want to implement ML to monitor log file, classify them as normal and abnormal. The model must learn via this process of classification and after time be able to classify the log files itself. This classification of the log files is based on time of login and place of login. Any hint / advice of ready made model for the scenario would be of great help.
If you want to use only time of login and place of login, there are several models that would do the work. 
Take a look at Python's Sklearn. It offer lots of different options.
An example one is random forest: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

Normally you don't event need to understand the model under the hood to be able to use it, so no need to feel intimidated by the names