  Wireless sensor networks (WSNs) are composed of spatially distributed sensors
and are considered vulnerable to attacks by worms and their variants. Due to
the distinct strategies of worms propagation, the dynamic behavior varies
depending on the different features of the sensors. Modeling the spread of
worms can help us understand the worm attack behaviors and analyze the
propagation procedure. In this paper, we design a communication model under
various worms. We aim to learn our proposed model to analytically derive the
dynamics of competitive worms propagation. We develop a new searching space
combined with complex neural network models. Furthermore, the experiment
results verified our analysis and demonstrated the performance of our proposed
learning algorithms.
