  In the recent years, the problem of identifying suspicious behavior has
gained importance and identifying this behavior using computational systems and
autonomous algorithms is highly desirable in a tactical scenario. So far, the
solutions have been primarily manual which elicit human observation of entities
to discern the hostility of the situation. To cater to this problem statement,
a number of fully automated and partially automated solutions exist. But, these
solutions lack the capability of learning from experiences and work in
conjunction with human supervision which is extremely prone to error. In this
paper, a generalized methodology to predict the hostility of a given object
based on its movement patterns is proposed which has the ability to learn and
is based upon the mechanism of humans of learning from experiences. The
methodology so proposed has been implemented in a computer simulation. The
results show that the posited methodology has the potential to be applied in
real world tactical scenarios.
