Network forensics: random infection vs spreading epidemic

Authors:
Chris Milling;Constantine Caramanis;Shie Mannor;Sanjay Shakkottai
Affiliations:
The University of Texas at Austin, Austin, TX, USA;The University of Texas at Austin, Austin, TX, USA;Technion, Haifa, Israel;The University of Texas at Austin, Austin, TX, USA
Venue:
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Year:
2012

Citing 5
Cited 1

A faster approximation algorithm for the Steiner problem in graphs

Information Processing Letters
Regular Article: The Diameter of Sparse Random Graphs

Advances in Applied Mathematics
Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics)

Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics)
Social networks and context-aware spam

Proceedings of the 2008 ACM conference on Computer supported cooperative work
Detecting sources of computer viruses in networks: theory and experiment

Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems

Detecting epidemics using highly noisy data

Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Computer (and human) networks have long had to contend with spreading viruses. Effectively controlling or curbing an outbreak requires understanding the dynamics of the spread. A virus that spreads by taking advantage of physical links or user-acquaintance links on a social network can grow explosively if it spreads beyond a critical radius. On the other hand, random infections (that do not take advantage of network structure) have very different propagation characteristics. If too many machines (or humans) are infected, network structure becomes essentially irrelevant, and the different spreading modes appear identical. When can we distinguish between mechanics of infection? Further, how can this be done efficiently? This paper studies these two questions. We provide sufficient conditions for different graph topologies, for when it is possible to distinguish between a random model of infection and a spreading epidemic model, with probability of misclassification going to zero. We further provide efficient algorithms that are guaranteed to work in different regimes.