  The new generation of cyber threats leverages advanced AI-aided methods,
which make them capable to launch multi-stage, dynamic, and effective attacks.
Current cyber-defense systems encounter various challenges to defend against
such new and emerging threats. Modeling AI-aided threats through game theory
models can help the defender to select optimal strategies against the attacks
and make wise decisions to mitigate the attack's impact. This paper first
explores the current state-of-the-art in the new generation of threats in which
AI techniques such as deep neural network is used for the attacker and
discusses further challenges. We propose a Markovian dynamic game that can
evaluate the efficiency of defensive methods against the AI-aided attacker
under a cloud-based system in which the attacker utilizes an AI technique to
launch an advanced attack by finding the shortest attack path. We use the CVSS
metrics to quantify the values of this zero-sum game model for decision-making.

Computer Science and Game Theory