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International Conference on Financial Cryptography and Data Security

FC 2012: Financial Cryptography and Data Security pp 53–67Cite as

Enabling Users to Self-manage Networks: Collaborative Anomaly Detection in Wireless Personal Area Networks

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 7398))

Abstract

Personal area networks such as home or small office LANs are usually more vulnerable to cyber-attacks than those with dedicated support staff and the ability to invest consistently in security defenses. In this paper I propose leveraging physical characteristics of these personal area networks in order to enable non-technical individuals to secure their networks or at least be aware that their devices have been compromised. This proposal leverages records of location for mobile devices, proximity authentication, and individual homophily. In this work, I summarize previous studies on securing personal networks, proximity authentication, and software attestation. I then present a preliminary design for the detection of and recovery from infection for personal area networks. Limitations and future work are also discussed.

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Author information

Authors and Affiliations

  1. School of Informatics and Computing, Indiana University, Bloomington, IN, USA

    Zheng Dong

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  1. Zheng Dong
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Editor information

Editors and Affiliations

  1. USC Information Sciences Institute, 4676 Admiralty Way, Suite 1001, 90292, Marina del Rey, CA, USA

    Jim Blyth

  2. Department of Computer Science, Stevens Institute of Technology, Castle Point on Hudson, 07030, Hoboken, NJ, USA

    Sven Dietrich

  3. Indiana University, 414 E First St, 47401, Bloomington, IN, USA

    L. Jean Camp

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Dong, Z. (2012). Enabling Users to Self-manage Networks: Collaborative Anomaly Detection in Wireless Personal Area Networks. In: Blyth, J., Dietrich, S., Camp, L.J. (eds) Financial Cryptography and Data Security. FC 2012. Lecture Notes in Computer Science, vol 7398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34638-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-34638-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34637-8

  • Online ISBN: 978-3-642-34638-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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