Detection of Web Defacements by means of Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/IEEEias/MedvetFB07,
-
title = "Detection of Web Defacements by means of Genetic
Programming",
-
author = "Eric Medvet and Cyril Fillon and Alberto Bartoli",
-
booktitle = "Third International Symposium on Information Assurance
and Security, IAS 2007",
-
year = "2007",
-
editor = "Ning Zhang and Ajith Abraham",
-
pages = "227--234",
-
address = "Manchester",
-
month = "29-31 " # aug,
-
publisher = "IEEE",
-
keywords = "genetic algorithms, genetic programming, Internet, Web
sites, computer crime, Web detection, Web pages, Web
site defacement, domain-specific knowledge,
evolutionary computation",
-
DOI = "doi:10.1109/IAS.2007.13",
-
abstract = "Web site defacement, the process of introducing
unauthorized modifications to a Web site, is a very
common form of attack. Detecting such events
automatically is very difficult because Web pages are
highly dynamic and their degree of dynamism may vary
widely across different pages. In this paper we propose
a novel detection approach based on genetic programming
(GP), an established evolutionary computation paradigm
for automatic generation of algorithms. What makes GP
particularly attractive in this context is that it does
not rely on any domain-specific knowledge, whose
description and synthesis is invariably a hard job. In
a preliminary learning phase, GP builds an algorithm
based on a sequence of readings of the remote page to
be monitored and on a sample set of attacks. Then, we
monitor the remote page at regular intervals and apply
that algorithm, which raises an alert when a suspect
modification is found. We developed a prototype based
on a broader Web detection framework we proposed
earlier and we tested our approach over a dataset of 15
dynamic Web pages, observed for about a month, and a
collection of real Web defacements. We compared the
results to those of a solution we developed earlier,
whose design embedded a substantial amount of domain
specific knowledge, and the results clearly show that
GP may be an effective approach for this job.",
-
notes = "http://www.ias07.org/ Also known as \cite{4299779}",
- }
Genetic Programming entries for
Eric Medvet
Cyril Fillon
Alberto Bartoli
Citations