Proactively applied encryption in multipath networks

Abstract

In providing data privacy on multipath networks, it is important to conserve bandwidth by ensuring that only the necessary level of encryption is applied to each path. This is achieved by dispersing data along multiple secure paths in such a way that the highest encryption level is applied to those paths where threats are most likely to be present. Conversely, for those paths where the likelihood of attack is least, the encryption levels should be commensurately lower. In order to maintain data privacy, path encryption level adjustments should be proactive. In so doing, the multipath network should have the ability to calculate the probability of an attack and proactively adjust the encryption strength long before the final steps of an attack sequence occur. The unique methods described in this research, are able to sense when an attack sequence is initiated on a path. This is achieved by calculating the probability of the presence of specific attack sequence signatures along each network path using statistical learning techniques, and by deriving path information assurance levels using these probabilities. As an attack sequence progresses, the likelihood of the presence of specific attacks grows until a threshold level is met and an encryption adjustment for a path is warranted.