A Robust Cybersecurity Topic Classification Tool
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866914692518117376 |
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| author | Pelofske, Elijah Liebrock, Lorie M. Urias, Vincent |
| author_facet | Pelofske, Elijah Liebrock, Lorie M. Urias, Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_02473 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Robust Cybersecurity Topic Classification Tool Pelofske, Elijah Liebrock, Lorie M. Urias, Vincent Information Retrieval Computation and Language Cryptography and Security Machine Learning 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. |
| title | A Robust Cybersecurity Topic Classification Tool |
| topic | Information Retrieval Computation and Language Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2109.02473 |