Learning Vector Quantization and Radial Basis Function Performance Comparison Based Intrusion Detection System
Contributo in Atti di convegno
Data di Pubblicazione:
2021
Citazione:
Learning Vector Quantization and Radial Basis Function Performance Comparison Based Intrusion Detection System / J.T. Hounsou, P.B.C. Niyomukiza, T. Nsabimana, G. Vlavonou, F. Frati, E. Damiani (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING). - In: Intelligent Human Systems Integration 2021 / [a cura di] D. Russo, T. Ahram, W. Karwowski, G. Di Bucchianico, R. Taiar. - [s.l] : Springer, 2021. - ISBN 9783030680169. - pp. 561-572 (( Intervento presentato al 4. convegno Intelligent Human Systems Integration tenutosi a Palermo nel 2021 [10.1007/978-3-030-68017-6_83].
Abstract:
Information system’s technologies increase rapidly and continuously due to the huge traffic and volume of data. Stored data need to be secured adequately and transferred safely through the computer network. Therefore the data transaction mechanism still exposed to the intrusion attack of which consequences remain unlikable. An intrusion can be understood as a set of actions that can compromise the three security purposes known as Confidentiality, Integrity and Availability (CIA) of resources and services. In order to face on these intrusions, an efficient and robust Intrusion Detection System (IDS) which can detect successfully the intrusion is strongly recommended. An IDS is a network/host security tool used for preventing and detecting malicious attacks which could make a system useless. The purpose of this paper is to implement network intrusion detection system based on machine learning using Artificial Neural Network algorithms specifically the Learning Quantization Vector and Radial Basis Function make the comparison on the performance between these two algorithms.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Intrusion Detection System; Artificial Neural Network; Learning Vector Quantization; Radial Basis Function
Elenco autori:
J.T. Hounsou, P.B.C. Niyomukiza, T. Nsabimana, G. Vlavonou, F. Frati, E. Damiani
Link alla scheda completa:
Titolo del libro:
Intelligent Human Systems Integration 2021