Anomaly detection or more generally outliers detection is one of the most popular and challenging subject in theoretical and applied machine learning. The main challenge is that in general we have access to very few labeled data or no labels at all. In this chapter, we present a new semi-supervised anomaly detection method called AnoRand by combining a deep learning architecture with random synthetic label generation. The proposed architecture has two building blocks: (1) a noise detection (ND) block composed of Multi-layer Perceptrons and (2) an autoencoder (AE) block. The main idea of this new architecture is to learn one class (e.g. the majority class in case of anomaly detection) as well as possible by taking advantage of the ability of auto encoders to represent data in a latent space and the ability of Multi-layer Perceptrons (MLP) to learn one class when the data is highly imbalanced. First, we create synthetic anomalies by randomly disturbing (add noise) few samples (e.g. 2%) from the training set. Second, we use the normal and the synthetic samples as input to our model. We compared the performance of the proposed method to 18 state-of-the-art unsupervised anomaly detection method on synthetic data sets and 57 real-world data sets. Our results show that this new method generally outperforms most of the state-of-the-art methods and has the best performance (AUC ROC and AUC PR) on the vast majority of reference datasets. We also tested our method in a supervised way by using the actual labels to train the model. The results show that it has very good performance compared to most of state-of-the-art supervised algorithms.