Biblio

This paper studies Wireless Information-Theoretic Security for low-speed mobility in autonomic networks. More specifically, the impact of user movement on the Probability of Non-Zero Secrecy Capacity and Outage Secrecy Capacity for different channel conditions has been investigated. This is accomplished by establishing a link between different user locations and the boundaries of information-theoretic secure communication. Human mobility scenarios are considered, and its impact on physical layer security is examined, considering quasi-static Rayleigh channels for the fading phenomena. Simulation results have shown that the Secrecy Capacity depends on the relative distance of legitimate and illegitimate (eavesdropper) users in reference to the given transmitter.

In the Internet-of-Things (IoT), users might share part of their data with different IoT prosumers, which offer applications or services. Within this open environment, the existence of an adversary introduces security risks. These can be related, for instance, to the theft of user data, and they vary depending on the security controls that each IoT prosumer has put in place. To minimize such risks, users might seek an “optimal” set of prosumers. However, assuming the adversary has the same information as the users about the existing security measures, he can then devise which prosumers will be preferable (e.g., with the highest security levels) and attack them more intensively. This paper proposes a decision-support approach that minimizes security risks in the above scenario. We propose a non-cooperative, two-player game entitled Prosumers Selection Game (PSG). The Nash Equilibria of PSG determine subsets of prosumers that optimize users' payoffs. We refer to any game solution as the Nash Prosumers Selection (NPS), which is a vector of probabilities over subsets of prosumers. We show that when using NPS, a user faces the least expected damages. Additionally, we show that according to NPS every prosumer, even the least secure one, is selected with some non-zero probability. We have also performed simulations to compare NPS against two different heuristic selection algorithms. The former is proven to be approximately 38% more effective in terms of security-risk mitigation.