  Security challenges accompany the efficiency. The pervasive integration of
information and communications technologies (ICTs) makes cyber-physical systems
vulnerable to targeted attacks that are deceptive, persistent, adaptive and
strategic. Attack instances such as Stuxnet, Dyn, and WannaCry ransomware have
shown the insufficiency of off-the-shelf defensive methods including the
firewall and intrusion detection systems. Hence, it is essential to design
up-to-date security mechanisms that can mitigate the risks despite the
successful infiltration and the strategic response of sophisticated attackers.
In this chapter, we use game theory to model competitive interactions between
defenders and attackers. First, we use the static Bayesian game to capture the
stealthy and deceptive characteristics of the attacker. A random variable
called the \textit{type} characterizes users' essences and objectives, e.g., a
legitimate user or an attacker. The realization of the user's type is private
information due to the cyber deception. Then, we extend the one-shot
simultaneous interaction into the one-shot interaction with asymmetric
information structure, i.e., the signaling game. Finally, we investigate the
multi-stage transition under a case study of Advanced Persistent Threats (APTs)
and Tennessee Eastman (TE) process. Two-Sided incomplete information is
introduced because the defender can adopt defensive deception techniques such
as honey files and honeypots to create sufficient amount of uncertainties for
the attacker. Throughout this chapter, the analysis of the Nash equilibrium
(NE), Bayesian Nash equilibrium (BNE), and perfect Bayesian Nash equilibrium
(PBNE) enables the policy prediction of the adversary and the design of
proactive and strategic defenses to deter attackers and mitigate losses.
