Open AccessClassification and Analysis of Malicious Traffic with Multi-layer Perceptron Model
Author(s) -
Shilpa P. Khedkar,
A. Ramalingam
Publication year - 2021
Publication title -
ingénierie des systèmes d'information/ingénierie des systèmes d'information
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.161
H-Index - 8
eISSN - 2116-7125
pISSN - 1633-1311
DOI - 10.18280/isi.260307
Subject(s) - traffic classification , computer science , quality of service , data mining , traffic generation model , perceptron , multilayer perceptron , intrusion detection system , provisioning , artificial intelligence , traffic analysis , network packet , classifier (uml) , machine learning , artificial neural network , computer network
Traffic classification is very important field of computer science as it provides network management information. Classification of traffic become complicated due to emerging technologies and applications. It is used for Quality of Service (QoS) provisioning, security and detecting intrusion in a system. In the past used of port, inspecting packet, and machine learning algorithms have been used widely, but due to the sudden changes in the traffic, their accuracy was diminished. In this paper a Multi-Layer Perceptron model with 2 hidden layers is proposed for traffic classification and target traffic classify into different categories. The experimental results indicate that proposed classifier efficiently classifies traffic and achieves 99.28% accuracy without feature engineering.