Applying CMAC-Based On-Line Learning to Intrusion Detection

James D. Cannady

doi:10.1109/IJCNN.2000.861503

Applying CMAC-Based On-Line Learning to Intrusion Detection

James D. Cannady

Research output: Contribution to journal › Conference article › peer-review

33 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 lean 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 by a modified reinforcement leaning method that uses feedback from the protected system.

Original language	American English
Journal	Proceedings of the 2000 IEEE/INNS Joint International Conference on Neural Networks
DOIs	https://doi.org/10.1109/IJCNN.2000.861503
State	Published - Jul 1 2000
Event	2000 IEEE/INNS Joint International Conference on Neural Networks - Duration: Jul 1 2000 → …

Keywords

CMAC
Intrusion detection
denial of service attacks

Disciplines

Computer Sciences

Access to Document

10.1109/IJCNN.2000.861503License: Unspecified

Cite this

@article{9c4025868dba4e4281edd3818a9a0017,

title = "Applying CMAC-Based On-Line Learning to Intrusion Detection",

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 lean 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 by a modified reinforcement leaning method that uses feedback from the protected system.",

keywords = "CMAC, Intrusion detection, denial of service attacks",

author = "Cannady, \{James D.\}",

year = "2000",

month = jul,

day = "1",

doi = "10.1109/IJCNN.2000.861503",

language = "American English",

journal = "Proceedings of the 2000 IEEE/INNS Joint International Conference on Neural Networks",

note = "2000 IEEE/INNS Joint International Conference on Neural Networks ; Conference date: 01-07-2000",

}

TY - JOUR

T1 - Applying CMAC-Based On-Line Learning to Intrusion Detection

AU - Cannady, James D.

PY - 2000/7/1

Y1 - 2000/7/1

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 lean 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 by a modified reinforcement leaning method that uses 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 lean 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 by a modified reinforcement leaning method that uses feedback from the protected system.

KW - CMAC

KW - Intrusion detection

KW - denial of service attacks

UR - https://nsuworks.nova.edu/gscis_facarticles/460

U2 - 10.1109/IJCNN.2000.861503

DO - 10.1109/IJCNN.2000.861503

M3 - Conference article

JO - Proceedings of the 2000 IEEE/INNS Joint International Conference on Neural Networks

JF - Proceedings of the 2000 IEEE/INNS Joint International Conference on Neural Networks

T2 - 2000 IEEE/INNS Joint International Conference on Neural Networks

Y2 - 1 July 2000

ER -

Applying CMAC-Based On-Line Learning to Intrusion Detection

Abstract

Keywords

Disciplines

Access to Document

Other files and links

Fingerprint

Cite this