Using Online Planning and Acting to Recover from Cyberattacks on Software-defined Networks

被引：0
|
作者：
Patra, Sunandita [1 ,2 ]
Velazquez, Alexander [3 ]
Kang, Myong [3 ]
Nau, Dana [1 ,2 ]
机构：
[1] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA
[2] Univ Maryland, Inst Syst Res, College Pk, MD 20742 USA
[3] Naval Res Lab, Informat Technol Div, Washington, DC 20375 USA
来源：
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2021年 / 35卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We describe ACR-SDN, a system to monitor, diagnose, and quickly respond to attacks or failures that may occur in software-defined networks (SDNs). An integral part of ACR-SDN is its use of RAE+UPOM, an automated acting and planning engine that uses hierarchical refinement. To advise ACR-SDN on how to recover a target system from faults and attacks, RAE+UPOM uses attack recovery procedures written as hierarchical operational models. Our experimental results show that the use of refinement planning in ACR-SDN is successful in recovering SDNs from attacks with respect to five performance metrics: estimated time for recovery, efficiency, retry ratio, success ratio, and costEffectiveness.
引用
收藏
页码：15377 / 15384
页数：8
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