PERFORMANCE ANALYSIS OF MACHINE LEARNING MODELS FOR THREATS AND ATTACKS IN NETWORK SECURITY TRAFFIC MODEL

P.Prasanya Devi Research Scholar, Department of Computer Application, School of Information Technology, Madurai Kamaraj University, Madurai, prasanyamsc@gmail.com
Dr.S.Kannan Professor, Department of Computer Application, School of Information Technology, Madurai Kamaraj University, Madurai, skannanmku@gmail.com

Abstract

In Internet Engineering Task Force 97 (IETF97), the challenge is introduced as networks suffer from the lack of a unified theory that can be applied to all networks. It means that the behavior of networks is heterogeneous based on their different topologies, equipment, scale, applications, etc. It causes an important problem that ML techniques should be trained for each network separately. The accuracy of Machine Learning(ML) techniques that are trained by public datasets can be reduced in different networks. There are some efforts to provide representative datasets, but it seems that the challenge increases the need for ML techniques that can label the data and re-train frequently for each network separately. Therefore, rather than public datasets, the Deep Learning(DL) techniques should be trained by exclusive datasets gathered from the target network and labeled with high accuracy. To solve this challenge, DL techniques should be learned for each network separately, but as mentioned above, retraining the DL models for each network is a time and resource consuming task.

Keywords:

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Machine Learning, Deep Learning, Algorithms in ML and DL, Deep Learning Threats and Attacks, Traffic Analysis, Traffic Prediction, Performance Analysis.

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