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http://dx.doi.org/10.13089/JKIISC.2020.30.3.455

Detection of NoSQL Injection Attack in Non-Relational Database Using Convolutional Neural Network and Recurrent Neural Network  

Seo, Jeong-eun (Korea University)
Moon, Jong-sub (Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.30, no.3, 2020 , pp. 455-464 More about this Journal
Abstract
With a variety of data types and high utilization of data, non-relational databases are a popular data storage because it supports better availability and scalability. The increasing use of this technology also brings the risk of NoSQL injection attacks. Existing works mostly discuss the rule-based detection of NoSQL injection attacks that it is hard to deal with NoSQL queries beyond the coverage of the rules. In this paper, we propose a model for detecting NoSQL injection attacks. Our model is based on deep learning algorithms that select features from NoSQL queries using CNN, and classify NoSQL queries using RNN. Also, we experiment the proposed model to compare with existing models, and find that our model outperforms traditional models in terms of detection rate.
Keywords
NoSQL injection; Deep Learning; Convolutional Neural Network(CNN); Recurrent Neural Network(RNN);
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