Exploration of Singular Spectrum Analysis for Online Anomaly Detection in CRNs

Dong, Qi and Yang, Zekun and Chen, Yu and Li, Xiaohua and Zeng, Kai (2017) Exploration of Singular Spectrum Analysis for Online Anomaly Detection in CRNs. EAI Endorsed Transactions on Security and Safety, 4 (12). e3. ISSN 2032-9393

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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.

Item Type: Article
Uncontrolled Keywords: Cognitive Radio Networks (CRNs), Anomaly Detection, Singular Spectrum Analysis (SSA)
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 26 Mar 2021 13:52
Last Modified: 26 Mar 2021 13:52
URI: https://eprints.eudl.eu/id/eprint/2074

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