Title :
A dataflow system for anomaly detection and analysis
Author :
Bara, Andrei ; Xinyu Niu ; Luk, Wayne
Author_Institution :
Dept. of Comput., Imperial Coll. London, London, UK
Abstract :
This paper proposes DeADA, a dataflow architecture incorporating an automated, unsupervised and online learning algorithm. Compared with 24 core software implementations, DeADA achieves up to 6.17 times lower data drop rate and 10.7 times higher power efficiency. More importantly, experimental results for the Heartbleed case study suggest that DeADA is capable of detecting unknown attacks under network speeds of at least 18Mbps, a feature which is essential for modern network intrusion detection.
Keywords :
data flow computing; security of data; unsupervised learning; DeADA; anomaly detection; automated learning algorithm; dataflow architecture; heartbleed; network intrusion detection; online learning algorithm; unknown attack detection; unsupervised learning algorithm; Accuracy; Algorithm design and analysis; Computer architecture; Field programmable gate arrays; Kernel; Random access memory; Support vector machines;
Conference_Titel :
Field-Programmable Technology (FPT), 2014 International Conference on
Print_ISBN :
978-1-4799-6244-0
DOI :
10.1109/FPT.2014.7082793
