Intrusion Detection in Web Applications: Evolutionary Approach

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@InProceedings{Skaruz:2009:IMCSIT,
  author =       "Jaroslaw Skaruz and Franciszek Seredynski",
  title =        "Intrusion Detection in Web Applications: Evolutionary
                 Approach",
  booktitle =    "International Multiconference on Computer Science and
                 Information Technology, IMCSIT '09",
  year =         "2009",
  month =        oct,
  pages =        "117--123",
  publisher =    "IEEE ?",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  URL =          "http://www.proceedings2009.imcsit.org/pliks/iv_imcsit.pdf",
  abstract =     "A novel approach based on applying a modern
                 metaheuristic Gene Expression Programming (GEP) to
                 detecting web application attacks is presented in the
                 paper. This class of attacks relates to malicious
                 activity of an intruder against applications, which use
                 a database for storing data. The application uses SQL
                 to retrieve data from the database and web server
                 mechanisms to put them in a web browser. A poor
                 implementation allows an attacker to modify SQL
                 statements originally developed by a programmer, which
                 leads to stealing or modifying data to which the
                 attacker has not privileges. While the attack consists
                 in modification of SQL queries sent to the database,
                 they are the only one source of information used for
                 detecting attacks. Intrusion detection problem is
                 transformed into classification problem, which the
                 objective is to classify SQL queries between either
                 normal or malicious queries. GEP is used to find a
                 function used for classification of SQL queries.
                 Experimental results are presented on the basis of SQL
                 queries of different length. The findings show that the
                 efficiency of detecting SQL statements representing
                 attacks depends on the length of SQL statements.
                 Additionally we studied the impact of classification
                 threshold on the obtained results.",
  notes =        "Institute of Computer Science, University of Podlasie,
                 Sienkiewicza 51, 08-110 Siedlce, Poland",
}

Genetic Programming entries for Jaroslaw Skaruz Franciszek Seredynski

Citations