• D-SCIDS : Distributed soft com...

Result: D-SCIDS : Distributed soft computing intrusion detection system

Title:
D-SCIDS : Distributed soft computing intrusion detection system
Authors:
ABRAHAM, Ajith, JAIN, Ravi, THOMAS, Johnson, SANG YONG HAN
Source:
Journal of network and computer applications. 30(1):81-98
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, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Electronique, Electronics, Matériel informatique, Hardware, Informatique répartie, Distributed computer systems, Telecommunications et theorie de l'information, Telecommunications and information theory, 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, Agent intelligent, Intelligent agent, Agente inteligente, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Apprentissage, Learning, Aprendizaje, Arbre décision, Decision tree, Arbol decisión, Calcul réparti, Distributed computing, Cálculo repartido, Classification automatique, Automatic classification, Clasificación automática, Classification signal, Signal classification, Détecteur intrus, Intruder detector, Detector intruso, Implémentation, Implementation, Implementación, Machine vecteur support, Support vector machine, Máquina vector soporte, Monitorage, Monitoring, Monitoreo, Programmation linéaire, Linear programming, Programación lineal, Surveillance, Vigilancia, Système aide décision, Decision support system, Sistema ayuda decisíon, Système expert, Expert system, Sistema experto, Système réparti, Distributed system, Sistema repartido, Sécurité, Safety, Seguridad, Télédétection, Remote sensing, Teledetección, Télésurveillance, Remote supervision, Televigilancia
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Computer Science and Engineering, Chung-Ang University, Korea, Republic of
University of South Australia, Adelaide, Australia
Computer Science Department, Oklahoma State University, OK 74106, United States
ISSN:
1084-8045
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18276070
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.18276070
Database:
PASCAL Archive

Further Information

An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This paper evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results are then compared with other machine learning techniques like decision trees, support vector machines and linear genetic programming. Further, we modeled Distributed Soft Computing-based IDS (D-SCIDS) as a combination of different classifiers to model lightweight and more accurate (heavy weight) IDS. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.