  It is important to predict any adversarial attacks and their types to enable
effective defense systems. Often it is hard to label such activities as
malicious ones without adequate analytical reasoning. We propose the use of
Hidden Markov Model (HMM) to predict the family of related attacks. Our
proposed model is based on the observations often agglomerated in the form of
log files and from the target or the victim's perspective. We have built an
HMM-based prediction model and implemented our proposed approach using Viterbi
algorithm, which generates a sequence of states corresponding to stages of a
particular attack. As a proof of concept and also to demonstrate the
performance of the model, we have conducted a case study on predicting a family
of attacks called Action Spoofing.
