Molecular, Neural and Bacterial Networks Provide Insights For Computer Network Security

The robust defenses that yeast cells have evolved to protect themselves from environmental threats hold lessons that can be used to design computer networks and analyze how secure they are, say computer scientists at Carnegie Mellon University.

Environmental “noise” is a key evolutionary pressure that shapes the interconnections within cells, as well as those of neural networks and bacterial/ecological networks, they observe in a paper to be published online April 30 by the Journal of the Royal Society Interface. The researchers factored this into an established model for the evolution of molecular connections, resulting in an algorithm that gives rise to a rich range of architectures found in biological, computer and other types of networks.
Saket Navlakha, a post-doctoral researcher in CMU’s Machine Learning Department, said this approach is particularly helpful in understanding how networks respond to cascading failures, whether it be an overloaded power grid or a computer network being overwhelmed by fake identities in a so-called sybil attack.

The generative model the CMU team developed can be used to tailor networks to the environments in which they are expected to operate. These strike a balance between highly connected networks that are efficient and fast but are prone to infections and cascading failures, such as the Internet and its large service providers, and more sparsely connected elements that are less efficient, requiring more time to relay information, but can better tolerate failures and attacks, such as peer to peer networks.
By modifying an existing duplication-divergence model to account for the pressure of environmental noise, the researchers developed a method that can be used to generate or evaluate the interconnection, or topology, of networks that work in a variety of environments.

You can view the full press release here.