Learning the Propagation of Worms in Wireless Sensor Networks
This work addresses security vulnerabilities in wireless sensor networks, but it appears incremental as it builds on existing modeling approaches with new learning methods.
The paper tackled the problem of modeling worm propagation in wireless sensor networks by designing a communication model and learning algorithms, with experiments verifying the analysis and demonstrating performance.
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.