Enhancing Machine Learning Based SQL Injection Detection Using Contextualized Word Embedding

Author:

Zulu Janet1ORCID,Han Bonian2ORCID,Alsmadi Izzat1ORCID,Liang Gongbo1ORCID

Affiliation:

1. Texas A&M University-San Antonio, San Antonio, Texas, USA

2. Hangzhou Dianzi University, Hangzhou, Zhejiang, China

Publisher

ACM

Reference46 articles.

1. Analysis of a Random Forests Model;Biau Gérard;The Journal of Machine Learning Research,2012

2. Leo Breiman. 2001. Random Forests. Machine learning 45 (2001), 5--32.

3. Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, et al. 2013. API Design for Machine Learning Software: Experiences from the Scikit-Learn Project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. Prague, Czech Republic, 108--122.

4. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186.

5. Rational Decisions

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