摘要
With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures,a machine learning based Intrusion Detection Systems(IDS)has become a vital component to protect our economic and national security.Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection.The single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion patterns.Particularly,the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples.In order to further enhance the performance of machine learning based IDS,we propose the Big Data based Hierarchical Deep Learning System(BDHDLS).BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload.Each deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one cluster.This strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches.Based on parallel training strategy and big data techniques,the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.
基金
partially supported by Research Initiative for Summer Engagement(RISE)from the Office of the Vice President for Research at University of South Carolina
作者简介
Corresponding author:Wei Zhong received the PhD degree in computer science from Georgia State University,USA,in 2006.He is a full professor in the Division of Math and Computer Science,University of South Carolina Upstate.He is an elected fellow of International Society of Intelligent Biological Medicine.He is also the IEEE senior member.His research interests include deep learning,data mining,bioinformatics,and high performance computing.E-mail:wzhong@uscupstate.edu;Ning Yu currently is an assistant professor at the State University of New York College at Brockport,USA.He earned the PhD degree in computer science from Georgia State University in 2016 and has published more than 20 papers in prestigious journals,such as IEEE Transactions,BMC Bioinformatics,and PLOS One.His current research focuses on big data analytics,deep learning,network and information security,information processing,and high performance computing.E-mail:nyu@brockport.edu;Chunyu Ai received the BS and MS degrees in computer science from Heilongjiang University,China in 2001 and 2004,respectively,and the PhD degree in computer science from Georgia State University,USA in 2010.She is currently an associate professor in the Division of Math and Computer Science,University of South Carolina Upstate.Her research interests include wireless sensor networks,data management,machine learning,and social networks.E-mail:aic@uscupstate.edu.
