# Learning the Propagation of Worms in Wireless Sensor Networks

arXiv: https://arxiv.org/abs/2209.09984 · HTML: https://mlsanity.com/paper/2209.09984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [A Survey of Complex-Valued Neural Networks](https://mlsanity.com/paper/2101.12249/) (2021)
- [Learnability of Competitive Threshold Models](https://mlsanity.com/paper/2205.03750/) (2022)

## Built on by (most cited first, 1 of 1)

- [Machine Learning Epidemic Predictions Using Agent-based Wireless Sensor Network Models](https://mlsanity.com/paper/2511.15982/) (2025)

## Abstract (arXiv)

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.