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Software define network (SDN) is an
opensource network plane which define routing paths for network traffic based
on OpenFlow protocol. The SDN technology depends on three layers application,
controller and infrastructure and these layers link with each other by using
Southbound API and Northbound API.

The scope behind the work defined in this
article is the advancement of an SDN network management application for uniqueness
of networks that uses machine learning. The goal is to observe how traffic
information can be learned by an SDN controller using SDN framework. The
researchers of this paper concentrating on TCP traffic since most of
applications of interest in an enterprise network are transported over TCP.

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I.     Litrature

A Deep-Reinforcement Learning Approach for
Software-Defined Networking Routing Optimization

In this paper research design and assess a
Deep-Reinforcement Learning agent that enhances and improve routing. In this
scenario agent adapts automatically to current traffic circumstances and
proposes tailored formations that attempt to minimize the network delay. Trials
show very promising performance. This approach offers important operational
advantages with respect to outdated optimization algorithms1.

Predicting Network Attack Patterns in SDN using Machine
Learning Approach

A trial setup of 32 honeypots reported 17M
login attempts initiating from 112 different states and over 6000 distinct
source IP addresses. Due to decoupled control and data plane, Software Defined
Networks (SDN) can handle these increasing number of attacks by blocking those
network connections at the switch level. The bid deal is to stop the malicious
connections existing in the entire network. Past network attack data can be
used to robotically identify and block the malevolent connections. There are
infrequent existing open-source software tools to inspect and bound the number
of login attempts per source IP address one-by-one. Though, these explanations
cannot capably act against a chain of attacks that includes multiple IP
addresses used by each attacker. In this paper, they suggest using machine
learning algorithms, trained on historical network attack data, to identify the
potential malicious connections and potential attack destinations. They use
four widely-known machine learning algorithms: C4.5, Bayesian Network (BayesNet.2.

iii.      QoS-Aware Adaptive
Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement
Learning Approach

Software Defined Networks (SDN) known as the
next generation networking example that decouples the data forwarding from the unified
control. To understand the merits of devoted QoS provisioning and fast route reconfiguration
facilities over the decoupled SDN, various QoS requirements in pack delay,
loss, and output should be maintained by an effective transportation with
respect to each application. In this paper, a QoS recognize adaptive routing (QAR)
scheme is projected in the designed multi-layer ordered SDNs. The scattered classified
control plane architecture is employed to minimalize signaling delay in large
SDNs via three-levels strategy of controllers, i.e., the super, domain (or
master), and slave controllers3.

Machine Learning in Software Defined Networks: Data
Collection and Traffic Classification

Software define network (SDN) is an
opensource network plane which define routing paths for network traffic depend
on OpenFlow protocol. The SDN depends on three SDN layers 1st application,
2nd controller & the 3rd infrastructure and every
layer communicate with each other by using Southbound API and Northbound API.

In this paper researchers use numerous
machine learning techniques to train the whole network based on traffic
classification. The outcomes need that high accuracy classification can be accomplished
with the data-sets using supervised learning4.

II.    Comparison
between sdn and other

TCP flows are perceived by inspecting the
flags in the payload of the packet in OpenFlow messages received in the
controller. The size and timestamps of the first five packets are also directly
obtained by storing the size of the Packet In payload and the arrival time of
the messages. The inter-arrival times of the first five packets is then
computed from that information.

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