Hybrid Intrusion Forecasting Framework for Early Warning System

Sehun KIM; Seong-jun SHIN; Hyunwoo KIM; Ki Hoon KWON; Younggoo HAN

doi:10.1093/ietisy/e91-d.5.1234

Hybrid Intrusion Forecasting Framework for Early Warning System

Sehun KIM, Seong-jun SHIN, Hyunwoo KIM, Ki Hoon KWON, Younggoo HAN

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Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

Publication: IEICE TRANSACTIONS on Information Vol.E91-D No.5 pp.1234-1241

Publication Date: 2008/05/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e91-d.5.1234

Type of Manuscript: Special Section INVITED PAPER (Special Section on Information and Communication System Security)

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Sehun KIM, Seong-jun SHIN, Hyunwoo KIM, Ki Hoon KWON, Younggoo HAN, "Hybrid Intrusion Forecasting Framework for Early Warning System" in IEICE TRANSACTIONS on Information, vol. E91-D, no. 5, pp. 1234-1241, May 2008, doi: 10.1093/ietisy/e91-d.5.1234.
Abstract: Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.5.1234/_p

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@ARTICLE{e91-d_5_1234,
author={Sehun KIM, Seong-jun SHIN, Hyunwoo KIM, Ki Hoon KWON, Younggoo HAN, },
journal={IEICE TRANSACTIONS on Information},
title={Hybrid Intrusion Forecasting Framework for Early Warning System},
year={2008},
volume={E91-D},
number={5},
pages={1234-1241},
abstract={Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.},
keywords={},
doi={10.1093/ietisy/e91-d.5.1234},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Hybrid Intrusion Forecasting Framework for Early Warning System
T2 - IEICE TRANSACTIONS on Information
SP - 1234
EP - 1241
AU - Sehun KIM
AU - Seong-jun SHIN
AU - Hyunwoo KIM
AU - Ki Hoon KWON
AU - Younggoo HAN
PY - 2008
DO - 10.1093/ietisy/e91-d.5.1234
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2008
AB - Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.
ER -