Abstract
Cognitive radio networks (CRNs) have been recognized as a promising technology that allows secondary users (SUs) extensively explore spectrum resource usage efficiency, while not introducing interference to licensed users. Due to the unregulated wireless network environment, CRNs are susceptible to various malicious entities. Thus, it is critical to detect anomalies in the first place. However, from the perspective of intrinsic features of CRNs, there is hardly in existence of an universal applicable anomaly detection scheme. Singular Spectrum Analysis (SSA) has been theoretically proven an optimal approach for accurate and quick detection of changes in the characteristics of a running (random) process. In addition, SSA is a model-free method and no parametric models have to be assumed for different types of anomalies, which makes it a universal anomaly detection scheme. In this paper, we introduce an adaptive parameter and component selection mechanism based on coherence for basic SSA method, upon which we built up a sliding window online anomaly detector in CRNs. Our experimental results indicate great accuracy of the SSA-based anomaly detector for multiple anomalies.
Highlights
The rigid spectrum allocation scheme regulated by governmental agencies leads to great deficit on spectrum band resources
The simulation environment only considered two anomalies: primary user emulation (PUE) attack and PU abnormality, our proposed method can be used for many other anomaly detections, such as spectrum sensing data falsification (SSDF) and jamming, because those anomaly activities will inevitably deteriorate communication condition of cognitive radio networks (CRNs)
The delay is generally caused by two factors: 1) our light-weighted anomaly detector requires Secondary Users (SUs)’ data aggregation at fusion center (FC) for every several seconds (4s in our simulated online detection scenario); this relative low report time resolution will bring a small amount of detection delay; 2) our detection method is built upon transportation-layer-based data flow, which is not very sensitive to physical layer anomaly activities
Summary
The rigid spectrum allocation scheme regulated by governmental agencies leads to great deficit on spectrum band resources. In order to meet the two requirements, and according to Federal Communications Commission (FCC): “no modification to the incumbent signal should be required to accommodate opportunistic use of the spectrum by Secondary Users (SUs)” [2], CRN system is expected to collect and process sufficient, highly accurate information of the spectrum environment, without imposing overhead on incumbent users by adding new features, such as redundant symbolic pads, or authentication protocols. This dynamic Big Data processing task is very challenging.
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