The Application of Fuzzy ARTMAP in the Detection of Computer Network Attacks

James D. Cannady; Raymond C. Garcia

doi:10.1007/3-540-44668-0_32

The Application of Fuzzy ARTMAP in the Detection of Computer Network Attacks

James D. Cannady
, Raymond C. Garcia

Research output: Contribution to conference › Presentation

6 Scopus citations

Abstract

The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existing intrusion detection approaches require either manual coding of new attacks in expert systems or the complete retraining of a neural network to improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly using feedback from the protected system.

Original language	American English
DOIs	https://doi.org/10.1007/3-540-44668-0_32
State	Published - Aug 1 2001
Event	International Conference on Artificial Neural Networks - Duration: Aug 1 2001 → …

Conference

Conference	International Conference on Artificial Neural Networks
Period	8/1/01 → …

Keywords

Intrusion Detection
Radial Basis Function Neural Network
Intrusion Detection System
Cerebellar Model Articulation Controller
Adaptive Neural Network

Disciplines

Computer Sciences

Access to Document

10.1007/3-540-44668-0_32License: CC BY

Cite this

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title = "The Application of Fuzzy ARTMAP in the Detection of Computer Network Attacks",

abstract = " The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existing intrusion detection approaches require either manual coding of new attacks in expert systems or the complete retraining of a neural network to improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly using feedback from the protected system.",

keywords = "Intrusion Detection, Radial Basis Function Neural Network, Intrusion Detection System, Cerebellar Model Articulation Controller, Adaptive Neural Network",

author = "Cannady, \{James D.\} and Garcia, \{Raymond C.\}",

year = "2001",

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doi = "10.1007/3-540-44668-0\_32",

language = "American English",

note = "International Conference on Artificial Neural Networks ; Conference date: 01-08-2001",

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T1 - The Application of Fuzzy ARTMAP in the Detection of Computer Network Attacks

AU - Cannady, James D.

AU - Garcia, Raymond C.

PY - 2001/8/1

Y1 - 2001/8/1

N2 - The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existing intrusion detection approaches require either manual coding of new attacks in expert systems or the complete retraining of a neural network to improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly using feedback from the protected system.

AB - The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existing intrusion detection approaches require either manual coding of new attacks in expert systems or the complete retraining of a neural network to improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly using feedback from the protected system.

KW - Intrusion Detection

KW - Radial Basis Function Neural Network

KW - Intrusion Detection System

KW - Cerebellar Model Articulation Controller

KW - Adaptive Neural Network

UR - https://nsuworks.nova.edu/gscis_facpres/557

UR - https://nsuworks.nova.edu/gscis_facarticles/457

U2 - 10.1007/3-540-44668-0_32

DO - 10.1007/3-540-44668-0_32

M3 - Presentation

T2 - International Conference on Artificial Neural Networks

Y2 - 1 August 2001

ER -

The Application of Fuzzy ARTMAP in the Detection of Computer Network Attacks

Abstract

Conference

Keywords

Disciplines

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