Anormallik tespitinde çift yönlü üretken çekişmeli ağlar yaklaşımının geliştirilmesi için bir yöntem

Anomaly detection is considered as a challenging task due to its imbalanced and unlabelled nature. Numerous machine learning methods are applicable to the anomaly detection task. Conventional machine learning algorithms, such as supervised anomaly detection methods require labeled data sets and can obtain reasonable achievements on balanced data sets. However, they mostly suffer from the class imbalance problem. Unsupervised anomaly detection methods, on the other hand, assume that the more significant part of the data is normal and are inclined to label the least fit instances as anomalies. Semi-supervised methods create structures from normal data, which represents standard data distribution. To overcome this challenge, the combination of different machine learning approaches such as supervised, unsupervised, semi-supervised learning are proposed in the literature. With the advent of neural networks and generative models, different methodologies derived from neural networks are applied to anomaly detection tasks. In this study, we use the KDDCUP99 and Credit Card Fraud Detection data set, consider them as an anomaly detection task, and implement Bidirectional Generative Adversarial Networks, considering it as a one-class anomaly detection algorithm. Since generator and discriminator are highly dependent on each other in the training phase, to reduce this dependency, in this paper, we propose three different training approaches for Bidirectional Generative Adversarial Networks by adding extra training steps to it. We also demonstrate that proposed approaches increased the performance of Bidirectional Generative Adversarial Networks on anomaly detection task.}