In this paper, we propose a robust election simulation model and
independently developed election anomaly detection algorithm that demonstrates
the simulation's utility. The simulation generates artificial elections with
similar properties and trends as elections from the real world, while giving
users control and knowledge over all the important components of the elections.
We generate a clean election results dataset without fraud as well as datasets
with varying degrees of fraud. We then measure how well the algorithm is able
to successfully detect the level of fraud present. The algorithm determines how
similar actual election results are as compared to the predicted results from
polling and a regression model of other regions that have similar demographics.
We use k-means to partition electoral regions into clusters such that
demographic homogeneity is maximized among clusters. We then use a novelty
detection algorithm implemented as a one-class Support Vector Machine where the
clean data is provided in the form of polling predictions and regression
predictions. The regression predictions are built from the actual data in such
a way that the data supervises itself. We show both the effectiveness of the
simulation technique and the machine learning model in its success in
identifying fraudulent regions.

本文提出了强健的选举模拟模型和独立开发的选举异常检测算法，使用人工生成选举结果数据集进行欺诈检测，并使用 K-means 算法进行分类和检测模型来检测选举结果的欺诈性。