Treffer: A prevention model for algorithmic complexity attacks

Title:
A prevention model for algorithmic complexity attacks
Authors:
KHAN, Suraiya, TRAORE, Issa
Source:
DIMVA 2005 : detection of intrusions and malware, and vulnerability assessment (Vienna, 7-8 July 2005)Lecture notes in computer science. :160-173
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 7 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Gestion des mémoires et des fichiers (y compris la protection et la sécurité des fichiers), Memory and file management (including protection and security), Analyse régression, Regression analysis, Análisis regresión, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Cryptanalyse, Cryptanalysis, Criptoanálisis, Déni service, Denial of service, Denegación de servicio, Détecteur intrus, Intruder detector, Detector intruso, Modèle régression, Regression model, Modelo regresión, Modélisation, Modeling, Modelización, Méthode cas pire, Worst case method, Método caso peor, Raisonnement basé sur modèle, Model-based reasoning, Réutilisation, Reuse, Reutilización, Système détection intrusion, Intrusion detection systems, Sécurité informatique, Computer security, Seguridad informatica, Temps exécution, Execution time, Tiempo ejecución, Vulnérabilité, Vulnerability, Vulnerabilidad
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Electrical and Computer Engineering, University of Victoria, PO Box 3055 STN CSC, Victoria BC V8W 3P6, Canada
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17010951
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17010951
Database:
PASCAL Archive

Weitere Informationen

Denial of Service (DoS) attack has been identified in security surveys as the second largest cause of monetary loss. Hence, DoS is a very important problem that needs to be dealt with seriously. Many DoS attacks are conducted by generating extremely high rate traffic; these are classified as flooding attacks. Other DoS attacks, which are caused by resource consumption, belong to the so-called logic attacks category, one such example is algorithmic complexity attack. Complexity attacks generate traffic containing data, which exploits the working principle of the algorithms running on a machine. In such an attack, a request imposes worst-case execution time on a resource and repeatedly reuses the same resource for further services. In this paper, we propose a regression analysis based model that can prevent algorithmic complexity attacks. We demonstrate our model on quick-sort algorithm.