Performing joint learning for passive intrusion detection in pervasive wireless environments

Jie Yang, Yong Ge, Hui Xiong, Yingying Chen, Hongbo Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

59 Scopus citations


Recent years have witnessed increasing interests in passive intrusion detection for wireless environments, e.g., asset protection in industrial facilities and emergency rescue of trapped people. Most previous studies have focused primarily on exploiting a single intrusion indicator, such as moving variance, for capturing an intrusion pattern at a time. However, in real-world, there are many intrusion patterns which may be only detectable by combining different intrusion indicators and performing detection jointly. To this end, we propose a joint intrusion learning approach, which has the ability in combining the detection power of several complementary intrusion indicators and detects different intrusion patterns at the same time. We developed the GREEK algorithm, which utilizes grid-based clustering over K-neighborhood to effectively diagnose the presence of intrusions. Further, we show that the performance of intrusion detection can be enhanced by utilizing the collaborative detecting efforts among multiple transmitter-receiver pairs. To validate the effectiveness of the joint intrusion learning method, we conducted experiments in a real-office environment using an IEEE 802.15.4 (Zigbee) network. Our experimental results provide strong evidence of the effectiveness of our joint learning approach in performing passive intrusion detection with a minimized false positive rate.

Original languageEnglish (US)
Title of host publication2010 Proceedings IEEE INFOCOM
StatePublished - 2010
Externally publishedYes
EventIEEE INFOCOM 2010 - San Diego, CA, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Country/TerritoryUnited States
CitySan Diego, CA

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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