Network Science in NDSSL at Virginia Tech 2009 HPC User Forum 21 April 2009 Roanoke, Virginia

Two Examples Wireless epidemics Epidemic simulations

What people do to study this? Wireless Epidemics 20.7 million smart devices were shipped in North America in 2007 and more expected in years to come Expected growth of 150% in smart phone market Ubiquity of smart digital devices lead to amplified opportunities for malware attacks Open APIs result in buggy third party applications for smart devices What people do to study this? Use mathematical modeling to predict the impact Useful and scalable, but not accurate Simulation studies of the wireless networks with worm implemented Accurate, not scalable (500 nodes -> 2 days) We build models for studying them through a parallel discrete event simulator Very little accuracy lost, but provides scalability (35000 -> 1 hour)

EpiNet Simulation Framework Activity-based mobility models device mobility Sub-location modeling constructs wireless networks at activity locations Malware manifestation models worm and protocol characteristics What do we study with this? Understand the spread Detection strategies Response schemes for handling outbreaks Evaluate strategies Conclusions Network structure significantly alter the spread More reason to consider realistic proximity networks Early/late interventions are not that different Dynamic interventions are required to be studied Graphic of comparison of early versus late interventions with degree rank

Avian Influenza: The Threat 9 regional outbreaks in the last 11 years Southeast Asia from 2003-2005 Economic loss of $10 billion US 62 deaths Global concern: influenza pandemic Possible fatalities: ~150 million deaths

Large Transmission System 145903 poultry farms in the US Farm sizes range from 1 to 5,000,000 birds Avian influenza: avian-to-avian transmission among farms One initially infected farm (node): #17196 ~2500 miles

Simulated Virus Diffusion time = t0 largest network size 144381 nodes 414 million edges initially infected node (red nodes have contracted virus)

Vulnerability Maps for 3 Networks Network M Network L phase transition < 2000 infections 1700-10000 infections > 10000 infections community ≡ nodes within a geographic zone possessing high vulnerability

What Gives Rise To a Large Attack Size? Red lines are boundaries between northern and southern communities Boundary region is an area of low vulnerability The virus must cross this divide for large attack size The aforementioned divide is the dominant one, but there are others Consequently, we have “speed up” and “slow down” of the virus spread network M, only those simulations resulting in large attack size