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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.


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Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection

Show Author's information Azidine Guezzaz( )Younes AsimiMourade AzrourAhmed Asimi
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.

Keywords:

anomaly detection, heterogeneous traffic, preprocessing, machine learning, training, classification
Received: 09 June 2020 Accepted: 25 August 2020 Published: 12 January 2021 Issue date: March 2021
References(17)
[1]
S. Y. Hao, J. Long, and Y. C. Yang, BL-IDS: Detecting web attacks using Bi-LSTM model based on deep learning, in Security and Privacy in New Computing Environments, J. Li, Z. L. Liu, and H. Peng, eds. Springer, 2019, pp. 551-563.
[2]
Y. Zhou and P. C. Wang, An ensemble learning approach for XSS attack detection with domain knowledge and threat intelligence, Comp. Secur., vol. 82, pp. 261-269, 2019.
[3]
S. Rupam, A. Verma, and A. Singh, An approach to detect packets using packet sniffing, Int. J. Comp. Sci. Eng. Surv., vol. 4, no. 3, pp. 21-25, 2013.
[4]
L. Igual and S. Seguín, Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications. Springer, 2017.
[5]
O. K. Sahingoza, E. Buberb, O. Demirb, and B. Diri, Machine learning based phishing detection from URLs, Expert Syst. Appl., vol. 117, pp. 345-357, 2019.
[6]
S. Raschka and V. Mirjalili, Python Machine Learning. 2nd ed. Birmingham, UK: Packt Publishing, 2017.
[7]
S. B. Kotsiantis, I. D. Zaharakis, and P. E. Pintelas, Machine learning: A review of classification and combining techniques, Artif. Intell. Rew., vol. 26, no. 3, pp. 159-190, 2006.
[8]
A. Guezzaz, A. Asimi, Y. Sadqi, Y. Asimi, and Z. Tbatou, A new hybrid network sniffer model based on pcap language and sockets (Pcapsocks), Int. J. Adv. Comp. Sci. Appl., vol. 7, no. 2, pp. 207-214, 2016.
[9]
A. Guezzaz, A. Asimi, Y. Asimi, Z. Tbatous, and Y. Sadqi, A global intrusion detection system using PcapSockS sniffer and multilayer perceptron classifier, Int. J. Netw. Secur., vol. 21, no. 3, pp. 438-450, 2019.
[10]
V. N. Vapnik, An overview of statistical learning theory, IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 988-999, 1999.
[11]
F. Lauer and G. Bloch, Méthodes SVM pour l’identication, , 2006.
[12]
M. Rochaa, P. Cortezb, and J. Nevesa, Evolution of neural networks for classification and regression, Neurocomputing, vol. 70, nos. 16-18, pp. 2809-2816, 2007.
[13]
M. Idhammad, K. Afdel, and M. Belouch, Detection system of HTTP DDoS attacks in a cloud environment based on information theoretic entropy and random forest, Hindawi Secur. Commun. Netw., vol. 2018, p. 1263123, 2018.
[14]
A. Guezzaz, A. Asimi, M. Azrour, Z. Batou, and Y. Asimi, A multilayer perceptron classifier for monitoring network traffic, in Big Data and Networks Technologies, Y. Farhaoui, ed. Springer, 2020.
[15]
Y. Farhaoui and A. Asimi, Performance method of assessment of the intrusion detection and prevention systems, Int. J. Eng. Sci. Technol., vol. 3, no. 7, pp. 5916-5928, 2011.
[16]
B. B. Yong, X. Liu, Q. C. Yu, L. Huang, and Q. G. Zhou, Malicious web traffic detection for internet of things environments, Comp. Electr. Eng., vol. 77, pp. 260-272, 2019.
[17]
M. ul-Hassan, M. A. Khan, K. Mahmood, and A. M. Shah. Analysis of IPv4 vs IPv6 traffic in US, Int. J. Adv. Comp. Sci. Appl., vol. 7, no. 12, pp. 261-267, 2016.
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Publication history

Received: 09 June 2020
Accepted: 25 August 2020
Published: 12 January 2021
Issue date: March 2021

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© The author(s) 2021

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