  With the rapid development of Internet and the sharp increase of network
crime, network security has become very important and received a lot of
attention. We model security issues as stochastic systems. This allows us to
find weaknesses in existing security systems and propose new solutions.
Exploring the vulnerabilities of existing security tools can prevent
cyber-attacks from taking advantages of the system weaknesses. We propose a
hybrid network security scheme including intrusion detection systems (IDSs) and
honeypots scattered throughout the network. This combines the advantages of two
security technologies. A honeypot is an activity-based network security system,
which could be the logical supplement of the passive detection policies used by
IDSs. This integration forces us to balance security performance versus cost by
scheduling device activities for the proposed system. By formulating the
scheduling problem as a decentralized partially observable Markov decision
process (DEC-POMDP), decisions are made in a distributed manner at each device
without requiring centralized control. The partially observable Markov decision
process (POMDP) is a useful choice for controlling stochastic systems. As a
combination of two Markov models, POMDPs combine the strength of hidden Markov
Model (HMM) (capturing dynamics that depend on unobserved states) and that of
Markov decision process (MDP) (taking the decision aspect into account).
Decision making under uncertainty is used in many parts of business and
science.We use here for security tools.We adopt a high-quality approximation
solution for finite-space POMDPs with the average cost criterion, and their
extension to DEC-POMDPs. We show how this tool could be used to design a
network security framework.
