Rogue Signal Threat on Trust-based Cooperative Spectrum Sensing in Cognitive Radio Networks

ROGUE SIGNAL THREAT ON TRUST-BASED COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS By David Scott Jackson, Ph.D. A dissertation proposal submitted in partial fulfillment of the requirements for the degree of Ph.D. at Virginia Commonwealth University. Virginia Commonwealth University, 2015. Major Director: Meng Yu, Ph.D. Associate Professor, Department of Computer Science Cognitive Radio Networks (CRNs) are a next generation network that is expected to solve the wireless spectrum shortage problem, which is the shrinking of available wireless spectrum resources needed to facilitate future wireless applications. The first CRN standard, the IEEE 802.22, addresses this particular problem by allowing CRNs to share geographically unused TV spectrum to mitigate the spectrum shortage. Equipped with reasoning and learning engines, cognitive radios operate autonomously to locate unused channels to maximize its own bandwidth and Quality-of-Service (QoS). However, their increased capabilities over traditional radios introduce a new dimension of security threats. In an NSF 2009 workshop, the FCC raised the question, What authentication mechanisms are needed to support cooperative cognitive radio networks? Are reputationbased schemes useful supplements to conventional Public Key Infrastructure (PKI) authentication protocols? Reputation-based schemes in cognitive radio networks are a popular technique for performing robust and accurate spectrum sensing without any inter-communication with licensed networks, but the question remains on how effective they are at satisfying the FCC security requirements. xi Our work demonstrates that trust-based Cooperative Spectrum Sensing (CSS) protocols are vulnerable to rogue signals, which creates the illusion of inside attackers and raises the concern that such schemes are overly sensitive Intrusion Detection Systems (IDS). The erosion of the sensor reputations in trust-based CSS protocols makes CRNs vulnerable to future attacks. To counter this new threat, we introduce community detection and cluster analytics to detect and negate the impact of rogue signals on sensor reputations.