This paper presents a tutorial for network anomaly detection, focusing on non-signature-based approaches. Network traffic anomalies are unusual and significant changes in the traffic of a network. Networks play an important role in today's social and economic infrastructures. The security of the network becomes crucial, and network traffic anomaly detection constitutes an important part of network security. In this paper, we present three major approaches to non-signature-based network detection: PCA-based, sketch-based, and signal-analysis-based. In addition, we introduce a framework that subsumes the three approaches and a scheme for network anomaly extraction. We believe network anomaly detection will become more important in the future because of the increasing importance of network security.
In this paper, we present a compressed sensing based framework for a wireless sensor based biological sensing system for recording the frequency of fish electric organ discharge. We investigate the trade-offs between the parameters of compressed sensing, such as sampling matrix, reconstructed signal's signal to noise ratio, and compression ratio. The measured results show that our framework can be used to reduce the transmitted data size by 70% (3x) while maintaining at least 10 dB signal to noise ratio for the reconstructed time domain frequency data. This size reduction with acceptable reconstructed signal quality will help reduce transmission energy and storage requirements for long-term sensing experiments, thus enabling the use of small and low power wireless sensors.
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