The Effect of Decoy Attacks on Dynamic Channel Assignment
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As networks grow rapidly denser with the introduction of wireless-enabled cars, wearables and appliances, signal interference coupled with limited radio spectrum availability emerges as a significant hindrance to network performance. In order to retain high network throughput, channels must be strategically assigned to nodes in a way that minimizes signal overlap between neighboring nodes. Current static techniques for channel assignment are intolerant of network variations and growth, but flexible dynamic assignment techniques are becoming more feasible with the introduction of software defined networks and network function virtualization. Virtualized networks abstract hardware functions to software, making tasks such as channel assignment much more reactive and suitable for automation. As network maintenance tasks are increasingly handled by software, however, network stability becomes susceptible to malicious behavior. In this thesis, we expose and study the effect of stealthy attacks that aim to trigger unnecessary channel switching in a network and increase signal interference. We develop a Markov Decision Problem (MDP) framework and investigate suboptimal attack policies applied to a number of real-world topologies. We derive attack policies as an approximate MDP solution due to the exponentially large state space. Determining vulnerabilities to stealthy attacks is necessary in order to improve the security and stability of software defined networks.