sesa 17(12): e3

Research Article

Exploration of Singular Spectrum Analysis for Online Anomaly Detection in CRNs

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  • @ARTICLE{10.4108/eai.28-12-2017.153516,
        author={Qi Dong and Zekun Yang and Yu Chen and Xiaohua Li and Kai Zeng},
        title={Exploration of Singular Spectrum Analysis for Online Anomaly Detection in CRNs},
        journal={EAI Endorsed Transactions on Security and Safety},
        keywords={Cognitive Radio Networks (CRNs), Anomaly Detection, Singular Spectrum Analysis (SSA).},
  • Qi Dong
    Zekun Yang
    Yu Chen
    Xiaohua Li
    Kai Zeng
    Year: 2017
    Exploration of Singular Spectrum Analysis for Online Anomaly Detection in CRNs
    DOI: 10.4108/eai.28-12-2017.153516
Qi Dong1, Zekun Yang1, Yu Chen1,*, Xiaohua Li1, Kai Zeng2
  • 1: Dept. of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902
  • 2: Volgenau School of Engineering, George Mason University, Fairfax, VA 22030
*Contact email:


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.