  Encounter-based network is a frequently-disconnected wireless ad-hoc network
requiring immediate neighbors to store and forward aggregated data for
information disseminations. Using traditional approaches such as gateways or
firewalls for deterring worm propagation in encounter-based networks is
inappropriate. We propose the worm interaction approach that relies upon
automated beneficial worm generation aiming to alleviate problems of worm
propagations in such networks. To understand the dynamic of worm interactions
and its performance, we mathematically model worm interactions based on major
worm interaction factors including worm interaction types, network
characteristics, and node characteristics using ordinary differential equations
and analyze their effects on our proposed metrics. We validate our proposed
model using extensive synthetic and trace-driven simulations. We find that, all
worm interaction factors significantly affect the pattern of worm propagations.
For example, immunization linearly decreases the infection of susceptible nodes
while on-off behavior only impacts the duration of infection. Using realistic
mobile network measurements, we find that encounters are bursty, multi-group
and non-uniform. The trends from the trace-driven simulations are consistent
with the model, in general. Immunization and timely deployment seem to be the
most effective to counter the worm attacks in such scenarios while cooperation
may help in a specific case. These findings provide insight that we hope would
aid to develop counter-worm protocols in future encounter-based networks.
