Feature Selection and Implementation of IDS using Boosting algorithm

Autor:	Utpal Shrivastava, Neelam Sharma
Rok vydání:	2020
Předmět:	Artificial neural network Network security business.industry Computer science Feature selection Intrusion detection system computer.software_genre Set (abstract data type) Data mining Pattern matching business Database transaction computer Host (network)
Zdroj:	2020 International Conference on Computational Performance Evaluation (ComPE).
Popis:	Monitoring of the data traffic is done by Intrusion detection system (IDS) in the network and identify possibility of attacks with can cause harm in the network. The growing digital age where so many host are connected to network and digital transaction take place, it becomes important to secure one’s data in the network. In the proposed work, NSL-KDD train dataset in ration 8:2 is used to train and test a model. To identify the impact of different set of dataset features considered by comparing the accuracy calculated.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::90362c2b035557446f623c1580239609 https://doi.org/10.1109/compe49325.2020.9200072 Zobrazit plný text záznamu