Practical problems in anomaly detection where the number of normal data is extremely high compared to abnormal data
The presumption I feel you might be making is that a Machine Learning model is required for this task, but to be honest statistical approaches and less fashionable algorithms are available.
The reality is training a ML model on  your data is going to be a struggle because of the significant class imbalance and low volume of anomalies. That is why pretrained models are attractive, in particular Neural Networks.
To get a feel of the range of options, I find the Python library pyOD a great resource. It has about 30 different ways to detect outliers.