Robust Detection of Rogue Signals in Cooperative Spectrum Sensing

Cognitive radio networks can sense, detect, and monitor their surrounding radio frequency conditions including the interference and availability of a broad range of channels, followed by choosing the best possible channel for a given task. This is called Dynamic Spectrum Access (DSA) and it is a key characteristic of cognitive radios that enable them to operate on unused licensed channels. However, increased flexibility and convenience often leads to greater security attack vectors. Since cognitive radio networks perceive their surrounding environment through the physical layer in order to make decisions, they become vulnerable to rogue signals. Depending on how rogue signals are used, they can achieve Primary User Emulation, Sensory Manipulation, or Rogue Signal Framing (RSF) attacks. Our work focuses on accurately detecting rogue signals to mitigate the damage of RSF attacks on trust-based Cooperative Spectrum Sensing (CSS) protocols. We devised a community-detection clustering algorithm to distinguish between malicious/malfunctioning sensors and well-behaved sensors affected by rogue signals. Our rogue signal detection improves upon previous work through the use of dynamic clustering methods on a group of sensors based on the network’s size and density over a particular region. This gives the advantage of a one-size-fits-all solution when it comes to handling networks that are sparse, dense, and disproportionate. Additionally, we ran extensive tests that demonstrated an upward of 6% to 40% improvement, depending on the scenario parameters, in detecting rogue signals.