@Article{iasc.2020.010114,
AUTHOR = {B. Indira, K. Valarmathi},
TITLE = {A Perspective of the Machine Learning Approach for the Packet  Classification in the Software Defined Network},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {795--805},
URL = {http://www.techscience.com/iasc/v26n4/40284},
ISSN = {2326-005X},
ABSTRACT = {Packet classification is a major bottleneck in Software Defined Network (SDN). 
Each packet has to be classified based on the action specified in each rule in the
given flow table. To perform classification, the system requires much of the CPU 
clock time. Therefore, developing an efficient packet classification algorithm is 
critical for high speed inter networking. Existing works make use of exact 
matching, range matching and longest prefix matching for classification and
these techniques sometime enlarges rule databases, thus resulting in huge
memory consumption and inefficient searching performance. In order to select an
efficient packet classification algorithm with less memory consumption and high
classification accuracy, Machine Learning (ML) algorithms are used. For
performance comparison, ML algorithms are used, namely Multi-layer Perceptron 
(MLP), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), 
AdaBoost classifier (AB) and Support Vector Machine (SVM). All these 
algorithms build network for packet classification and train the network with the 
use of Access Control List (ACL) netbench dataset. 5-features of IPv4 packet
header are used and the algorithms classify the packets based on action/flow of
each packet. Experimental results show that among six algorithms, RF algorithm 
gives better improvement in accuracyperformance for permitted packets.},
DOI = {10.32604/iasc.2020.010114}
}