MILAN: Multi-Modal Passive Intrusion Learning in Pervasive Wireless Environments

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Supported Students and Staff

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Project Summary

The widespread deployment of wireless communication systems creates unprecedented opportunities to impact our daily lives. Regardless of whether wireless infrastructures are used just for communication or as the basis for actual responses, large-scale wireless data provide increasing opportunities for detecting environmental changes caused by moving objects. Indeed, it is expected to develop the ability to make use of existing wireless infrastructure and sensing data to track moving objects which do not carry radio devices and may not even being aware of being tracked. However, these wireless data are dynamic and have complex data characteristics, such as multi-scale, multi-source and multi-modal. As these data become large and more detailed, new challenges are emerging for intrusion learning.

This project aims to develop effective and scalable multi-modal passive intrusion learning techniques that have the capability to detect and track device-free moving objects in pervasive wireless environments through adaptive learning in a collaborative way. In contrast to traditional techniques, which require pre-deployment of specialized hardware, and thus not easily deployed for unscheduled tasks and may not be scalable, this project leads to new insights into intrusion learning by mining on wireless environmental data, as well as leading to new approaches to device-free wireless localization, which can be used to assist a broad array of applications (e.g., identification of people trapped in a fire building during emergency evacuation). Project results are expected to open a new venue for integrating learning capabilities into emerging pervasive wireless fields. The educational component seeks to equip students with the necessary background and practical skills needed to contribute to information technology and have a practical impact on a large set of cross-section domains.

Project Impact

Publication

  1. Xiaojun Quan, Hui Xiong, Wenyu Dou, Liu Wenyin, Yong Ge. Link Graph Analysis for Business Site Selection, IEEE Computer, accepted, 2011.
  2. Byron Gao, Martin Ester, Hui Xiong, Oliver Schulte, Jinyi Cai The Minimum Consistent Subset Cover Problem: A Minimization View of Data Mining, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 24, No. 2, pp. 309-325, 2012.
  3. Junjie Wu, Hui Xiong, Chen Liu, and Jian Chen. A Generalization of Distance Functions for Fuzzy c-Means Clustering, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Fuzzy Systems (TFS), Vol. 20, No. 3, pp. 557-571, 2012.
  4. Zhongmou Li, Hui Xiong, Yanchi Liu Mining Blackhole and Volcano Patterns in Directed Graphs: A General Approach, Data Mining and Knowledge Discovery Journal, pp. 577 - 602, 2012.
  5. Xueying Li, Huanhuan Cao, Enhong Chen, Hui Xiong, Jilei Tian. BP-Growth: Searching Strategies for Efficient Behavior Pattern Mining. the 13th IEEE International Conference on Mobile Data Management (MDM 2012). pp. 238 - 247, 2012.
  6. Chuanren Liu, Tianming Hu, Yong Ge, Hui Xiong. Which Distance Metric is Right: An Evolutionary K-Means View. In Proceedings of the 2012 SIAM International Conference on Data Mining (SDM 2012). pp. 907 - 918, 2012.
  7. Qi Liu, Yong Ge, Zhongmou Li, Enhong Chen, Hui Xiong, Personalized Travel Package Recommendation, the 11th IEEE International Conference on Data Mining (ICDM 2011) (ICDM 2011), pp. 407-416, 2011. (ICDM Best Research Paper Award).
  8. Yong Ge, Hui Xiong, Chuanren Liu, and Zhi-Hua Zhou, A Taxi Driving Fraud Detection System, the 11th IEEE International Conference on Data Mining (ICDM 2011) (ICDM 2011), pp. 181-190, 2011.
  9. Yong Ge, Qi Liu, Hui Xiong, Alexander Tuzhilin, Jian Chen, Cost-aware Travel Tour Recommendation, the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011), pp. 983-991, 2011.
  10. Shiwei Zhu, Junjie Wu, Hui Xiong, and Guoping Xia, Scaling up Top-K Cosine Similarity Search, Data and Knowledge Engineering (DKE), Volume 70, Number 1, January 2011.
  11. Wenjun Zhou, Hui Xiong, Checkpoint Evolution for Volatile Correlation Computing, Machine Learning, Volume 83, Number 1, pp. 103 - 131, 2011.
  12. Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser,Michael J. Pazzani, An Energy-Efficient Mobile Recommender System , the 16th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, (KDD 2010), pp. 899 - 908, 2010. (Accepted for full presentation).
  13. Yong Ge, Hui Xiong, Zhi-Hua Zhou, Hasan Ozdemir, Jannite Yu, and K.C. Lee, TOP-EYE: Top-k Evolving Trajectory Outlier Detection, the 19th ACM Conference on Information and Knowledge Management, (CIKM 2010), pp 1733-1736, 2010.
  14. Zhongmou Li, Hui Xiong , Yanchi Liu, Aoying Zhou, Detecting Blackholes and Volcanoes in Directed Networks, the 10th IEEE International Conference on Data Mining, (ICDM 2010), pp. 294 - 303, 2010.
  15. Jie Yang, Yong Ge, Hui Xiong, Yingying Chen, and Hongbo Liu, Performing Joint Learning for Passive Intrusion Detection in Pervasive Wireless Environments, the 29th IEEE Conference on Computer Communications, (IEEE INFOCOM 2010), pp. 767-775, 2010.

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