Applying multivariate data analysis to identify key parameters of bi-directional attack flows

Autor:	Korakoch Wilailux, Sudsanguan Ngamsuriyaroj
Rok vydání:	2015
Předmět:	Engineering Exploratory data analysis Multivariate analysis Flow (mathematics) business.industry Anomaly (natural sciences) Principal component analysis Key (cryptography) Intrusion detection system Data mining business computer.software_genre computer
Zdroj:	2015 Asian Conference on Defence Technology (ACDT).
DOI:	10.1109/acdt.2015.7111611
Popis:	Flow export data has been intensively used in anomaly-based intrusion detection systems; however, we have limited understanding of the characteristics of bi-directional flow parameters with respect to the types of network attacks. To recognize the relationship between traffic parameters, we propose an empirical model which analyzes synthetically generated five network attacks within a closed environment, and perform exploratory data analysis using principal component analysis. The experimental results have identified relevant key parameters for selecting good candidates for intrusion detection analysis. The analysis capabilities of bi-directional flow parameters and their characteristics persisting in selected attacks have been diagnosed and revealed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::78496c642db29d73219a40a949713f5c https://doi.org/10.1109/acdt.2015.7111611 Zobrazit plný text záznamu