Database security: combining neural networks and classification approach

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Title Database security: combining neural networks and classification approach
Autor: Skaruz, Jarosław
URI: http://hdl.handle.net/11331/3493
Date: 2019
Źródło: Studia Informatica : systemy i technologie informacyjne. Nr 23 (2019), s. 95-115
Abstract: In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take the advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to a recurrent neural network (RNN) trained by backpropagation through time (BPTT) algorithm. Then, two coefficients of the rule are evaluated. The rule is used to interpret RNN output. In the testing phase RNN with the rule is examined against attacks and legal data to find out how evaluated rule affects efficiency of detecting attacks. All experiments were conducted on Jordan network. Experimental results show the relationship between the rule and a length of SQL queries.

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Uznanie autorstwa-Na tych samych warunkach 3.0 Polska Except where otherwise noted, this item's license is described as Uznanie autorstwa-Na tych samych warunkach 3.0 Polska

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