Detecting Web Application Attacks with Use of Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Skaruz:2009:cec,
  author =       "Jaroslaw Skaruz and Franciszek Seredynski",
  title =        "Detecting Web Application Attacks with Use of Gene
                 Expression Programming",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "2029--2035",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P120.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983190",
  abstract =     "In the paper we present a novel approach based on
                 applying a modern metaheuristic Gene Expression
                 Programming (GEP) to detecting web application attacks.
                 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.",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR",
}

Genetic Programming entries for Jaroslaw Skaruz Franciszek Seredynski

Citations