ALDO: An anomaly detection framework for dynamic spectrum access networks

Song Liu, Yingying Chen, Wade Trappe, Larry J. Greenstein

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

66 Scopus citations


Dynamic spectrum access has been proposed as a means to share scarce radio resources, and requires devices to follow protocols that use resources in a proper, disciplined manner. For a cognitive radio network to achieve this goal, spectrum policies and the ability to enforce them are necessary. Detection of an unauthorized (anomalous) usage is one of the critical issues in spectrum etiquette enforcement. In this paper, we present a network structure for dynamic spectrum access and formulate the anomalous usage detection problem using statistical significance testing. The detection problem is classified into two subproblems. For the case where no authorized signal is present, we describe the existing cooperative sensing schemes and investigate the impact of signal path loss on their performance. For the case where an authorized signal is present, we propose three methods that detect anomalous transmissions by making use of the characteristics of radio propagation. Analytical models are formulated for two special cases and, due to the intractability of the general problem, we present an algorithm using machine learning techniques to solve the general case. Our simulation results show that our approaches can effectively detect unauthorized spectrum usage with high detection rate and low false positive rate.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2009 - The 28th Conference on Computer Communications
Number of pages9
StatePublished - 2009
Externally publishedYes
Event28th Conference on Computer Communications, IEEE INFOCOM 2009 - Rio de Janeiro, Brazil
Duration: Apr 19 2009Apr 25 2009

Publication series

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


Other28th Conference on Computer Communications, IEEE INFOCOM 2009
CityRio de Janeiro

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering


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