Paper Info
Title
Security and Performance Modeling and Optimization for Software Defined Networking
Abstract
Software Defined Networking (SDN) provides new functionalities to efficiently manage the network traffic, which can be used to enhance the networking capabilities to support the growing communication demands today. But at the same time, it introduces new attack vectors that can be exploited by attackers. Hence, evaluating and selecting countermeasures to optimize the security of the SDN is of paramount importance. However, one should also take into account the trade-off between security and performance of the SDN. In this paper, we present a security optimization approach for the SDN taking into account the trade-off between security and performance. We evaluate the security of the SDN using graphical security models and metrics, and use queuing models to measure the performance of the SDN. Further, we use Genetic Algorithms, namely NSGA-II, to optimally select the countermeasure with performance and security constraints. Our experimental analysis results show that the proposed approach can efficiently compute the countermeasures that will optimize the security of the SDN while satisfying the performance constraints.
Year
DOI
Venue
2019
10.1109/TrustCom/BigDataSE.2019.00087
2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Keywords
Field
DocType
Genetic Algorithm,Security Modeling,Security Optimization,Software Defined Networking
Countermeasure,Computer science,Computer network,Queueing theory,Software-defined networking,Genetic algorithm,Computer security model
Conference
ISSN
ISBN
Citations 
2324-898X
978-1-7281-2778-1
0
PageRank 
References 
Authors
0.34
13
5
Name
Order
Citations
PageRank
Taehoon Eom132.11
Jin B. Hong212017.50
SeongMo An322.10
Jong Sou Park438953.95
Dong Seong Kim586693.34