A network-based anomaly detection system using multiple network features

Yuji Waizumi; Yohei Sato; Yoshiaki Nemoto

A network-based anomaly detection system using multiple network features

Yuji Waizumi, Yohei Sato, Yoshiaki Nemoto

INF - Applied Information Sciences

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

Accuracy of anomaly-based intrusion detection greatly depends on features, the numerical values representing characteristics of network traffic. In order to increase accuracy, it is necessary to choose appropriate features that can correctly detect anomalous events. In this paper, we stress the fact that a specific kind of anomaly changes specific features. We propose a highly accurate and robust intrusion detection system using multiple features. Each feature is used for evaluating anomalous events independently by a statistical detection method. Through experiments, we investigate the accuracy of the proposed scheme.

Original language	English
Pages	410-413
Number of pages	4
Publication status	Published - 2007
Event	3rd International Conference on Web Information Systems and Technologies, Webist 2007 - Barcelona, Spain Duration: 2007 Mar 3 → 2007 Mar 6

Other

Other	3rd International Conference on Web Information Systems and Technologies, Webist 2007
Country	Spain
City	Barcelona
Period	07/3/3 → 07/3/6

Keywords

Anomaly Detection
Intrusion Detection System
Multiple Network Features
Principal Component Analysis

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A network-based anomaly detection system using multiple network features'. Together they form a unique fingerprint.

Cite this

@conference{ff82cb3bbea44638be07663359c3c4b2,

title = "A network-based anomaly detection system using multiple network features",

abstract = "Accuracy of anomaly-based intrusion detection greatly depends on features, the numerical values representing characteristics of network traffic. In order to increase accuracy, it is necessary to choose appropriate features that can correctly detect anomalous events. In this paper, we stress the fact that a specific kind of anomaly changes specific features. We propose a highly accurate and robust intrusion detection system using multiple features. Each feature is used for evaluating anomalous events independently by a statistical detection method. Through experiments, we investigate the accuracy of the proposed scheme.",

keywords = "Anomaly Detection, Intrusion Detection System, Multiple Network Features, Principal Component Analysis",

author = "Yuji Waizumi and Yohei Sato and Yoshiaki Nemoto",

note = "Copyright: Copyright 2009 Elsevier B.V., All rights reserved.; 3rd International Conference on Web Information Systems and Technologies, Webist 2007 ; Conference date: 03-03-2007 Through 06-03-2007",

year = "2007",

language = "English",

pages = "410--413",

}

TY - CONF

T1 - A network-based anomaly detection system using multiple network features

AU - Waizumi, Yuji

AU - Sato, Yohei

AU - Nemoto, Yoshiaki

PY - 2007

Y1 - 2007

N2 - Accuracy of anomaly-based intrusion detection greatly depends on features, the numerical values representing characteristics of network traffic. In order to increase accuracy, it is necessary to choose appropriate features that can correctly detect anomalous events. In this paper, we stress the fact that a specific kind of anomaly changes specific features. We propose a highly accurate and robust intrusion detection system using multiple features. Each feature is used for evaluating anomalous events independently by a statistical detection method. Through experiments, we investigate the accuracy of the proposed scheme.

AB - Accuracy of anomaly-based intrusion detection greatly depends on features, the numerical values representing characteristics of network traffic. In order to increase accuracy, it is necessary to choose appropriate features that can correctly detect anomalous events. In this paper, we stress the fact that a specific kind of anomaly changes specific features. We propose a highly accurate and robust intrusion detection system using multiple features. Each feature is used for evaluating anomalous events independently by a statistical detection method. Through experiments, we investigate the accuracy of the proposed scheme.

KW - Anomaly Detection

KW - Intrusion Detection System

KW - Multiple Network Features

KW - Principal Component Analysis

UR - http://www.scopus.com/inward/record.url?scp=67649826837&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67649826837&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:67649826837

SP - 410

EP - 413

T2 - 3rd International Conference on Web Information Systems and Technologies, Webist 2007

Y2 - 3 March 2007 through 6 March 2007

ER -

A network-based anomaly detection system using multiple network features

Abstract

Other

Keywords

ASJC Scopus subject areas

Other files and links

Cite this