Title :
Knowledge Discovery from Big Data for Intrusion Detection Using LDA
Author :
Jingwei Huang ; Kalbarczyk, Zbigniew ; Nicol, David M.
Author_Institution :
Inf. Trust Inst., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
fDate :
June 27 2014-July 2 2014
Abstract :
This paper explores a hybrid approach of intrusion detection through knowledge discovery from big data using Latent Dirichlet Allocation (LDA). We identify the "hidden" patterns of operations conducted by both normal users and malicious users from a large volume of network/systems logs, by mapping this problem to the topic modeling problem and leveraging the well established LDA models and learning algorithms. This new approach potentially completes the strength of signature-based and anomaly-based methods.
Keywords :
Big Data; data mining; learning (artificial intelligence); security of data; Big Data; LDA; LDA models; anomaly-based methods; intrusion detection; knowledge discovery; latent Dirichlet allocation; learning algorithms; network logs; signature-based methods; system logs; topic modeling problem; Big data; Data models; Intrusion detection; Knowledge discovery; Monitoring; Vocabulary; LDA; big data; data mining; intrusion detection;
Conference_Titel :
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4799-5056-0
DOI :
10.1109/BigData.Congress.2014.111
