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Anomaly detection or more generally outliers detection is one of the mostpopular and challenging subject in theoretical and applied machine learning.The main challenge is that in general we have access to very few labeled dataor no labels at all. In this paper, we present a new semi-supervised anomalydetection method called \textbf{AnoRand} by combining a deep learningarchitecture with random synthetic label generation. The proposed architecturehas two building blocks: (1) a noise detection (ND) block composed of feedforward ferceptron and (2) an autoencoder (AE) block. The main idea of this newarchitecture is to learn one class (e.g. the majority class in case of anomalydetection) as well as possible by taking advantage of the ability of autoencoders to represent data in a latent space and the ability of Feed ForwardPerceptron (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 syntheticsamples as input to our model. We compared the performance of the proposedmethod to 17 state-of-the-art unsupervised anomaly detection method onsynthetic datasets and 57 real-world datasets. Our results show that this newmethod generally outperforms most of the state-of-the-art methods and has thebest performance (AUC ROC and AUC PR) on the vast majority of referencedatasets. We also tested our method in a supervised way by using the actuallabels to train the model. The results show that it has very good performancecompared to most of state-of-the-art supervised algorithms.

AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling