Cyber security problem based on Multi-Objective Distributed Constraint Optimization technique

TL;DR: A novel algorithm for solving a cyber security problem which utilizes well-known and widely used branch and bound technique and depth-first search strategy and finds all trade-off solutions and the extension of this algorithm is proposed which utilizes a preprocessing technique called soft Arc Consistency.

Abstract: A cyber security problem is an important application domain for systems resilience. The increase of malware, computer viruses, and intensive cyber attacks are serious problems for our information society. In this paper, we introduce a new presentation of a cyber security problem. Our model is based on a Multi-Objective Distributed Constraint Optimization Problem (MO-DCOP) which is a fundamental problem that can formalize various applications related to multi-agent cooperation. MO-DCOP is suitable for modeling a cyber security problem, since cyber security problems involve multiple criteria, e.g., risk (security), surveillance (privacy) and cost. Furthermore, MO-DCOP is a decentralized model. In this model, variables and constraints are distributed among agents. Since there exists no single agent which maintains all informations, it is resilient against intensive cyber attacks. Furthermore, we develop a novel algorithm for solving a cyber security problem which utilizes well-known and widely used branch and bound technique and depth-first search strategy and finds all trade-off solutions. We also propose the extension of this algorithm which utilizes a preprocessing technique called soft Arc Consistency. The softAC is a well-known preprocessing technique which transforms a constraint optimization problem into a simplified problem that can be solved efficiently. In the experiments, we examine the run time of our proposed algorithms in cyber security problems and show that our algorithms can solve cyber security problems quickly.