Video gaming has gained huge popularity over the last few decades. As reported, there are about 2.9 billion gamers globally. Among all genres, competitive games are one of the most popular ones.

Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator is ineffective as an anomaly detector. In this paper, we propose modifications to the GAN loss such that the generated samples lie at the boundaries of the real data distribution. With our modified GAN loss, our anomaly detection method, called Fence GAN (FGAN), directly uses the discriminator score as an anomaly threshold. Our experimental results on the MNIST, CIFAR10 and KDD99 datasets show that FGAN yields the best anomaly classification accuracy compared to state-of-the-art methods.

In this paper, we present multiple parallelized support vector machines (MPSVMs), which aims to deal with the situation when multiple SVMs are required to be performed concurrently. The proposed MPSVM is based on an optimization procedure for nonnegative quadratic programming (NQP), called multiplicative updates. By using graphical processing units (GPUs) to parallelize the numerical procedure of SVMs, the proposed MPSVM showed good performance for a certain range of data size and dimension. In the experiments, we compared the proposed MPSVM with other cutting-edge implementations of GPU-based SVMs and it showed competitive performance. Furthermore, the proposed MPSVM is designed to perform multiple SVMs in parallel. As a result, when multiple operations of SVM are required, MPSVM can be one of the best options in terms of time consumption.