Big Data Mining and Analytics  2021, Vol. 4 Issue (1): 18-24    DOI: 10.26599/BDMA.2020.9020019
 Special Issue on Intelligent Recommendation System and Big Data Analysis
Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection
Department of Computer Science and Mathematics, High School of Technology, Cadi Ayyad University, Essaouira 44000, Morocco.
Department of Computer Science, High School of Technology, Ibn Zohr University, Guelmim 81000, Morocco.
IDMS Team, Department of Computer Science, Faculty of Science and Technology, Moulay Ismail University, Errachidia 52000, Morocco.
Department of Computer Science and Mathematics, Faculty of Sciences Agadir, Ibn Zohr University, Agadir 80000, Morocco.

Abstract

The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes. The recognition of new elements is possible based on predefined classes. Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities. To overcome this problem, new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic. The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks. The new model is based on a proposed machine learning algorithm that comprises an input layer, a hidden layer, and an output layer. A reliable training algorithm is proposed to optimize the weights, and a recognition algorithm is used to validate the model. Preprocessing is applied to the collected traffic prior to the analysis step. This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection.

Received: 09 June 2020      Published: 12 January 2021
Corresponding Authors: Azidine Guezzaz     E-mail: A.GUZZAZ@gmail.com