A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability
Mimic defense is an active defense theory, which aims to fundamentally change the “easy to attack and difficult to defend” situation of network security. In this paper, we propose an evaluation method based on the probability of being attack successfully, and improve the evalua...
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2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9199829/ |
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doaj-3598ff068ee8447599b7c3ede04f2c9f2021-03-30T03:33:37ZengIEEEIEEE Access2169-35362020-01-01818048118049010.1109/ACCESS.2020.30248479199829A Novel Model of Mimic Defense Based on Minimal L-Order Error ProbabilityGuoxi Chen0https://orcid.org/0000-0003-1021-9797Guang Shi1https://orcid.org/0000-0002-7868-7186Liquan Chen2https://orcid.org/0000-0002-7202-4939Xiaoyuan He3https://orcid.org/0000-0002-3549-8668Shengmao Jiang4https://orcid.org/0000-0002-9890-1098School of Automation, Southeast University, Nanjing, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaMimic defense is an active defense theory, which aims to fundamentally change the “easy to attack and difficult to defend” situation of network security. In this paper, we propose an evaluation method based on the probability of being attack successfully, and improve the evaluation scheme of historical confidence. We combine the two evaluation schemes with the TOPSIS (technique for order performance by similarity to ideal solution) algorithm, and finally form a complete heterogeneous variant dynamic scheduling model. Different from traditional multi-mode voting algorithms, the effect of the heterogeneous degree in voting is considered, and we use Bayesian estimation to obtain the optimal result in the probabilistic sense. Finally, simulation results show that the proposed algorithm can effectively enhance the dynamic and security of the mimic defense model, and give full play to the characteristics of mimic defense.https://ieeexplore.ieee.org/document/9199829/Mimic defenseheterogeneitydynamic schedulingTOPSIS algorithmBayesian estimation |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Guoxi Chen Guang Shi Liquan Chen Xiaoyuan He Shengmao Jiang |
| spellingShingle |
Guoxi Chen Guang Shi Liquan Chen Xiaoyuan He Shengmao Jiang A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability IEEE Access Mimic defense heterogeneity dynamic scheduling TOPSIS algorithm Bayesian estimation |
| author_facet |
Guoxi Chen Guang Shi Liquan Chen Xiaoyuan He Shengmao Jiang |
| author_sort |
Guoxi Chen |
| title |
A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability |
| title_short |
A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability |
| title_full |
A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability |
| title_fullStr |
A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability |
| title_full_unstemmed |
A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability |
| title_sort |
novel model of mimic defense based on minimal l-order error probability |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2020-01-01 |
| description |
Mimic defense is an active defense theory, which aims to fundamentally change the “easy to attack and difficult to defend” situation of network security. In this paper, we propose an evaluation method based on the probability of being attack successfully, and improve the evaluation scheme of historical confidence. We combine the two evaluation schemes with the TOPSIS (technique for order performance by similarity to ideal solution) algorithm, and finally form a complete heterogeneous variant dynamic scheduling model. Different from traditional multi-mode voting algorithms, the effect of the heterogeneous degree in voting is considered, and we use Bayesian estimation to obtain the optimal result in the probabilistic sense. Finally, simulation results show that the proposed algorithm can effectively enhance the dynamic and security of the mimic defense model, and give full play to the characteristics of mimic defense. |
| topic |
Mimic defense heterogeneity dynamic scheduling TOPSIS algorithm Bayesian estimation |
| url |
https://ieeexplore.ieee.org/document/9199829/ |
| work_keys_str_mv |
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1724183270422216704 |