Fuzzy frequent episodes for real-time intrusion detection

Autor:	Jianxiong Luo, Susan M. Bridges, R.B. Vaughn
Rok vydání:	2002
Předmět:	Association rule learning Anomaly-based intrusion detection system Computer science business.industry Fuzzy set Intrusion detection system Extension (predicate logic) computer.software_genre Machine learning Fuzzy logic Anomaly detection Data mining Artificial intelligence business computer
Zdroj:	FUZZ-IEEE
Popis:	Data mining methods including association rule mining and frequent episode mining have been applied to the intrusion detection problem. We describe an extension that uses fuzzy frequent episodes for near real-time intrusion detection. We first define fuzzy frequent episodes and then describe experiments that explore their applicability for real-time intrusion detection. Experimental results indicate that fuzzy frequent episodes can provide effective approximate anomaly detection.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9d4cc688ddc2c0bb1f3346e07c45fe48 https://doi.org/10.1109/fuzz.2001.1007325 Zobrazit plný text záznamu