یادگیری ماشین در شبکه نرم افزار محور
Abstract
I- INTRODUCTION
II- RELATED WORK
III- MACHINE LEARNING FOR SDN SECURITY
IV- MACHINE LEARNING FOR TRAFFIC CLASSIFICATION
V- CONCLUSIONS
REFERENCES
Abstract
As a new network architecture, software defined network (SDN) separates the control plane from the forwarding plane which enables administrators to define and control the network through the method of software programming, provides a new research direction for the next generation of network architecture. At the same time, the machine learning technology has been developed rapidly in recent years and some studies have begun to introduce machine learning methods into SDN to improve the efficiency of network management and conformity, or to solve problems that cannot be solved easily by traditional methods. The paper analyses, summarizes and introduces these researches which used the supervised learning, unsupervised learning or semi-supervised learning methods to solve some specific problems on SDN, and it will help later researchers understand the filed more quickly and promote the development of the machine learning technology in SDN.
INTRODUCTION
The machine learning is an important branch of artificial intelligence research area, and various machine learning algorithms such as Support Vector Machine (SVM) [1], KNearest Neighbor (KNN) [2], Logistic Regression (Logistic Regression) [3], Boosting [4], etc. have been widely used to solve complex problems in engineering and science fields. The emergences of big data and GPU technology provide more powerful support for the development of machine learning technology. The deep learning [5] proposed by Geoffrey Hinton et al. in 2006 pushed the machine learning to a new climax, and made machine learning rapidly develop into an independent area and be applied to various fields, such as pattern recognition, data mining, bioinformatics and autonomous driving, etc. Clark proposed a network architecture of “A Knowledge Plane for the Internet” in 2003, which relies on machine learning and cognitive technology to manipulate the network [6]. The knowledge plane (KP) would bring many benefits to the network and change the way we operate, optimize and troubleshoot the network. But the distributed network architecture results in that each node (i.e., switches, routers) only has a partial view of the entire system, which makes it a huge challenge to apply machine learning to the network. Logical centralized control will alleviate the complexity of learning in a distributed environment.