A Robust Cybersecurity Topic Classification Tool

TL;DR

This paper introduces a cybersecurity topic classification tool that combines 21 machine learning models via majority voting, significantly reducing false positives and negatives in detecting cybersecurity discussions across large text datasets.

Contribution

The paper presents a novel ensemble-based cybersecurity text classification tool that improves accuracy and scalability over individual models.

Findings

Majority voting reduces false positive and false negative rates.

The CTC tool is scalable to large datasets within hours.

Ensemble models outperform individual classifiers in cybersecurity text detection.

Abstract

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.