  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 paper, we present a new semi-supervised anomaly
detection method called \textbf{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 feed
forward ferceptron 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 Feed Forward
Perceptron (FFP) 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 17 state-of-the-art unsupervised anomaly detection method on
synthetic datasets and 57 real-world datasets. 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.
