3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Security in Cognitive Radio Networks: Threats and Mitigation

  • @INPROCEEDINGS{10.1109/CROWNCOM.2008.4562534,
        author={T. Charles Clancy and Nathan Goergen},
        title={Security in Cognitive Radio Networks: Threats and Mitigation},
        proceedings={3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2008},
        month={7},
        keywords={},
        doi={10.1109/CROWNCOM.2008.4562534}
    }
    
  • T. Charles Clancy
    Nathan Goergen
    Year: 2008
    Security in Cognitive Radio Networks: Threats and Mitigation
    CROWNCOM
    IEEE
    DOI: 10.1109/CROWNCOM.2008.4562534
T. Charles Clancy1,*, Nathan Goergen2,*
  • 1: Electrical and Computer Engineering, University of Maryland, College Park Laboratory for Telecommunications Sciences, US Department of Defense
  • 2: Electrical and Computer Engineering, University of Maryland, College Park
*Contact email: tcc@umd.edu, goergen@umd.edu

Abstract

This paper describes a new class of attacks specific to cognitive radio networks. Wireless devices that can learn from their environment can also be taught things by malicious elements of their environment. By putting artificial intelligence in charge of wireless network devices, we are allowing unanticipated, emergent behavior, fitting a perhaps distorted or manipulated level of optimality. The state space for a cognitive radio is made up of a variety of learned beliefs and current sensor inputs. By manipulating radio sensor inputs, an adversary can affect the beliefs of a radio, and consequently its behavior. In this paper we focus primarily on PHY-layer issues, describing several classes of attacks and giving specific examples for dynamic spectrum access and adaptive radio scenarios. These attacks demonstrate the capabilities of an attacker who can manipulate the spectral environment when a radio is learning. The most powerful of which is a self-propagating AI virus that could interactively teach radios to become malicious. We then describe some approaches for mitigating the effectiveness of these attacks by instilling some level of “common sense” into radio systems, and requiring learned beliefs to expire and be relearned. Lastly we provide a road-map for extending these ideas to higher layers in the network stack.