10 research outputs found
Hybrid Epidemics - A Case Study on Computer Worm Conficker
Conficker is a computer worm that erupted on the Internet in 2008. It is
unique in combining three different spreading strategies: local probing,
neighbourhood probing, and global probing. We propose a mathematical model that
combines three modes of spreading, local, neighbourhood and global to capture
the worm's spreading behaviour. The parameters of the model are inferred
directly from network data obtained during the first day of the Conifcker
epidemic. The model is then used to explore the trade-off between spreading
modes in determining the worm's effectiveness. Our results show that the
Conficker epidemic is an example of a critically hybrid epidemic, in which the
different modes of spreading in isolation do not lead to successful epidemics.
Such hybrid spreading strategies may be used beneficially to provide the most
effective strategies for promulgating information across a large population.
When used maliciously, however, they can present a dangerous challenge to
current internet security protocols
Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history
Spatial spread of infectious diseases among populations via the mobility of
humans is highly stochastic and heterogeneous. Accurate forecast/mining of the
spread process is often hard to be achieved by using statistical or mechanical
models. Here we propose a new reverse problem, which aims to identify the
stochastically spatial spread process itself from observable information
regarding the arrival history of infectious cases in each subpopulation. We
solved the problem by developing an efficient optimization algorithm based on
dynamical programming, which comprises three procedures: i, anatomizing the
whole spread process among all subpopulations into disjoint componential
patches; ii, inferring the most probable invasion pathways underlying each
patch via maximum likelihood estimation; iii, recovering the whole process by
assembling the invasion pathways in each patch iteratively, without burdens in
parameter calibrations and computer simulations. Based on the entropy theory,
we introduced an identifiability measure to assess the difficulty level that an
invasion pathway can be identified. Results on both artificial and empirical
metapopulation networks show the robust performance in identifying actual
invasion pathways driving pandemic spread.Comment: 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetic
Epidemic and timer-based message dissemination in VANETs: A performance comparison
Data dissemination is among the key functions of Vehicular Ad-Hoc Networks (VANETs), and it has attracted much attention in the past decade. We address distributed, efficient, and scalable algorithms in the context of VANETs adopting the paradigm. We introduce an epidemic algorithm for message dissemination. The algorithm, named EPIC, is based on few assumptions, and it is very simple to implement. It uses only local information at each node, broadcast communications, and timers. EPIC is designed with the goal to reach the highest number of vehicles “infected” by the message, without overloading the network. It is tested on different scenarios taken from VANET simulations based on real urban environments (Manhattan, Cologne, Luxembourg). We compare our algorithm with a standard-based solution that exploits the contention-based forwarding component of the ETSI GeoNetworking protocol. On the other hand, we adapt literature based on a connected cover set to assess the near-optimality of our proposed algorithm and gain insight into the best selection of relay nodes as the size of the graph over which messages are spread scales up. The performance evaluation shows the behavior of EPIC and allows us to optimize the protocol parameters to minimize delay and overhead