Managing high volume data for network attack detection using real-time flow filtering

Autor:	Yitzchak M. Gottlieb, Aditya Naidu, Yukiko Sawaya, Ayumu Kubota, Abhrajit Ghosh, A. Yamada, Akshay Vashist, Alexander Poylisher
Rok vydání:	2013
Předmět:	Data processing Computer Networks and Communications Computer science business.industry Network security Real-time computing Volume (computing) Deep packet inspection Attack model Software Scalability Anomaly detection Electrical and Electronic Engineering business Computer network
Zdroj:	China Communications. 10:56-66
ISSN:	1673-5447
DOI:	10.1109/cc.2013.6488830
Popis:	In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::76ecc04b53f05218fbc77c416e90c55e https://doi.org/10.1109/cc.2013.6488830 Zobrazit plný text záznamu