Treffer: Modeling intrusion detection system using hybrid intelligent systems

Title:
Modeling intrusion detection system using hybrid intelligent systems
Source:
Journal of network and computer applications. 30(1):114-132
Publisher Information:
London: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 1 p.3/4
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Electronique, Electronics, Matériel informatique, Hardware, Systèmes informatiques, Computer systems, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Représentation du signal. Analyse spectrale, Signal representation. Spectral analysis, Télécommunications, Telecommunications, Systèmes, réseaux et services de télécommunications, Systems, networks and services of telecommunications, Services et terminaux de télécommunications, Services and terminals of telecommunications, Télémesure. Télésurveillance. Téléalarme. Télécommande, Telemetry. Remote supervision. Telewarning. Remote control, Apprentissage, Learning, Aprendizaje, Arbre décision, Decision tree, Arbol decisión, Classification automatique, Automatic classification, Clasificación automática, Classification signal, Signal classification, Complexité calcul, Computational complexity, Complejidad computación, Détecteur intrus, Intruder detector, Detector intruso, Machine vecteur support, Support vector machine, Máquina vector soporte, Modèle hybride, Hybrid model, Modelo híbrido, Modélisation, Modeling, Modelización, Monitorage, Monitoring, Monitoreo, Précision, Accuracy, Precisión, Surveillance, Vigilancia, Système hybride, Hybrid system, Sistema híbrido, Système informatique, Computer system, Sistema informático, Système intelligent, Intelligent system, Sistema inteligente, Sécurité, Safety, Seguridad, Télédétection, Remote sensing, Teledetección, Télésurveillance, Remote supervision, Televigilancia, Decision trees, Ensemble approach, Hybrid intelligent system, Intrusion detection system, Support vector machines
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Computer Science Department, Oklahoma State University, OK 74106, United States
School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea, Republic of
Deportment of Computer Science, Babes-Bolyai University, Cluj-Napoca 3400, Romania
ISSN:
1084-8045
Rights:
Copyright 2006 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

Electronics

Telecommunications and information theory
Accession Number:
edscal.18276072
Database:
PASCAL Archive

Weitere Informationen

The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). This paper presents two hybrid approaches for modeling IDS. Decision trees (DT) and support vector machines (SVM) are combined as a hierarchical hybrid intelligent system model (DT-SVM) and an ensemble approach combining the base classifiers. The hybrid intrusion detection model combines the individual base classifiers and other hybrid machine learning paradigms to maximize detection accuracy and minimize computational complexity. Empirical results illustrate that the proposed hybrid systems provide more accurate intrusion detection systems.