Automatic Detection of NoSQL Injection Using Supervised Learning

With the advancement in big data, NoSQL databases are enjoying ever-growing popularity. The increasing use of this technology in large applications also brings security concerns to the fore. Historically, SQL injection has been one of the major security threats over the years. Recent studies reveal that NoSQL databases also have become vulnerable to injections. However, NoSQL security is yet to receive the attention it deserves from the industry or academia. In this work, we develop a tool for detecting NoSQL injections using supervised learning. To the best of our knowledge, our developed training dataset on NoSQL injection is the first of its kind. We manually design important features and apply various supervised learning algorithms. Our tool has achieved 0.93 F
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-score as established by 10-fold cross-validation. We also apply our tool to a NoSQL injection generating tool, NoSQLMap and find that our tool outperforms Sqreen, the only available NoSQL injection detection tool, by 36.25% in terms of detection rate. The proposed technique is also shown to be database-agnostic achieving similar performance with injection on MongoDB and CouchDB databases.