I am trying to do a project using NLP. My goal is to process Cyber Threat Intelligence articles like this to extract information such as actor’s name, malwares and tools used…
To do that I want to use NER. However, there isn’t training data available on the web. So I was wondering if I should process manually 10-20 articles to make my training data or if I could do something like taking only interesting lines such as
“Rancor conducted at least two rounds of attacks intending to install Derusbi or KHRat malware on victim systems” in multiples articles and replacing the group name by another actor. This way I could deduplicate my training data by the number of known actors. But doing that, only the actor name is changing. So, the context is always the same.
I am wondering what’s the best way to train my model considering the quantity of training data available?