1 research outputs found
Proactive Defense for Internet-of-Things: Integrating Moving Target Defense with Cyberdeception
Resource constrained Internet-of-Things (IoT) devices are highly likely to be
compromised by attackers because strong security protections may not be
suitable to be deployed. This requires an alternative approach to protect
vulnerable components in IoT networks. In this paper, we propose an integrated
defense technique to achieve intrusion prevention by leveraging cyberdeception
(i.e., a decoy system) and moving target defense (i.e., network topology
shuffling). We verify the effectiveness and efficiency of our proposed
technique analytically based on a graphical security model in a software
defined networking (SDN)-based IoT network. We develop four strategies (i.e.,
fixed/random and adaptive/hybrid) to address "when" to perform network topology
shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based
optimization/random) to address "how" to perform network topology shuffling on
a decoy-populated IoT network, and analyze which strategy can best achieve a
system goal such as prolonging the system lifetime, maximizing deception
effectiveness, maximizing service availability, or minimizing defense cost. Our
results demonstrate that a software defined IoT network running our intrusion
prevention technique at the optimal parameter setting prolongs system lifetime,
increases attack complexity of compromising critical nodes, and maintains
superior service availability compared with a counterpart IoT network without
running our intrusion prevention technique. Further, when given a single goal
or a multi-objective goal (e.g., maximizing the system lifetime and service
availability while minimizing the defense cost) as input, the best combination
of "how" and "how" strategies is identified for executing our proposed
technique under which the specified goal can be best achieved.Comment: arXiv admin note: substantial text overlap with arXiv:1908.0032