1 / 9

Network Science in NDSSL at Virginia Tech

Explore the impact and spread of wireless epidemics on smart digital devices using mathematical modeling and simulation studies. The EpiNet Simulation Framework aids in understanding spread patterns, detection strategies, and response schemes, highlighting the importance of network structure and dynamic interventions.

jerrylane
Download Presentation

Network Science in NDSSL at Virginia Tech

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Network ScienceinNDSSLatVirginia Tech 2009 HPC User Forum 21 April 2009 Roanoke, Virginia

  2. Two Examples • Wireless epidemics • Epidemic simulations

  3. 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)

  4. 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

  5. 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

  6. ~2500 miles 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

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

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

  9. 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

More Related