Self-ad-MCNHA-SLOS - A Self-adaptive Minimum-Cost Network Hardening Algorithm based on Stochastic Loose Optimize Strategy

Yonglin Sun, Yongjun Wang, Yi Zhang

2012

Abstract

Given a network, it inevitable contains various vulnerabilities, which could be exploited by malicious attackers. It is an effective way to harden a network by searching and remedying those critical vulnerabilities. That is the so-called Minimum-Cost Network Hardening (MCNH) problem, but there haven’t any effective enough method to address this problem yet, especially, when facing large-scale network. We proposed Self-ad-MCNHA-SLOS, an algorithm using Stochastic Loose Optimize Strategy (SLOS) and self-adaptive parameter adjustment method ingeniously, to meet the problem. Experiment results show that it has the merits of high-efficiency, controllable, asymptotically optimal, and suitable for large-scale network.

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Paper Citation


in Harvard Style

Sun Y., Wang Y. and Zhang Y. (2012). Self-ad-MCNHA-SLOS - A Self-adaptive Minimum-Cost Network Hardening Algorithm based on Stochastic Loose Optimize Strategy . In Proceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2012) ISBN 978-989-8565-24-2, pages 372-378. DOI: 10.5220/0004022803720378

in Bibtex Style

@conference{secrypt12,
author={Yonglin Sun and Yongjun Wang and Yi Zhang},
title={Self-ad-MCNHA-SLOS - A Self-adaptive Minimum-Cost Network Hardening Algorithm based on Stochastic Loose Optimize Strategy},
booktitle={Proceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2012)},
year={2012},
pages={372-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004022803720378},
isbn={978-989-8565-24-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2012)
TI - Self-ad-MCNHA-SLOS - A Self-adaptive Minimum-Cost Network Hardening Algorithm based on Stochastic Loose Optimize Strategy
SN - 978-989-8565-24-2
AU - Sun Y.
AU - Wang Y.
AU - Zhang Y.
PY - 2012
SP - 372
EP - 378
DO - 10.5220/0004022803720378