I've got very little experience with neural networks and machine learning in general. I have a need to detect anomalies in XML documents. We have thousands of XML documents classified into 22 types. Each type of XML document is similar in structure. How do I go about detecting anomalies in XML structure among document types?
I've done simple outlier detection in time series and simpler structures, however, I'm lost at how to approach this for a tree-like structure of XML.
We've got different vendors that send us these XML files. Each with their own method of generating them. We want to be alerted on possible invalid file structures. We cannot use XML comparison algorithms directly since structures can acceptably vary a bit - a few extra nodes, a missing tag, etc. But the structure variances are consistent as well. So these variances should be trained against and not be classified as outliers during execution.
What we really want to catch are totally different structures - since vendors often send incorrect XML files mistakenly.