Anomaly Detection/Novelty detection I have a data-set that has over 6 million normal data and around 50 anomaly data. Those anomaly data is identified manually (by monitoring the user`s activity over camera and identify). I need to develop a model to detect these anomalies.
My problem is that the anomaly data looks like normal data, which means they are not outliers or has a certain pattern. If I plot the normal data over anomaly data they are in the same distribution.
I tried several anomaly detection approaches:

Multivariate Gaussian Distribution Approach to identify anomalies

I tried to create new features that anomaly data will be outliers and then I can use Multivariate Gaussian Distribution Approach, but could not able to find any combination to isolate the anomalies.


I guess there is no point of using a classification algorithm since dataset is highly imbalanced.

I tried OneClassSVM, DecisionTree, RandomForest but AUC is 0.5 (as good as random).



How to implement a model for this kind of scenario?
Other methods which I can think about:

Develop a NN with AutoEncoders
Try Generate Synthetic Samples and resample the dataset
I believe you can use a classification algorithm where you manually overrepresent the "anomalies" class. By how much, depends on the cost induced by the anomalies.

Just to illustrate what I mean:

Anomalies cover a continuum between two extremes: 1) those which can be safely ignored, because they induce no costs, and 2) those whose costs is unbearable. As an example close to the extreme (1), imagine a flower shop getting an order for Iris virginica. The customer will hardly object if, among 6 million flowers shipped, 50 actually belong to the species Iris versicolor.

On the other hand, if you are shipping airplane parts, and a faulty part would cause a disaster with 300+ dead, then you are closer to the extreme (2). You will likely want to avoid shipping even one faulty part.

In practice, it will have to be a trade-off: You can be on the safe side if you ship no parts at all, but then you go bankrupt. On the other hand, if you do no quality assurance at all, you ship enough faulty parts to cause a disaster in a foreseeable future, have to pay damages, and go bankrupt again.