In this research, we use user defined labels from three internet text sources
(Reddit, Stackexchange, Arxiv) to train 21 different machine learning models
for the topic classification task of detecting cybersecurity discussions in
natural text. We analyze the false positive and false negative rates of each of
the 21 model's in a cross validation experiment. Then we present a
Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of
the 21 trained machine learning models as the decision mechanism for detecting
cybersecurity related text. We also show that the majority vote mechanism of
the CTC tool provides lower false negative and false positive rates on average
than any of the 21 individual models. We show that the CTC tool is scalable to
the hundreds of thousands of documents with a wall clock time on the order of
hours.Comment: Improved formattin