Measuring Complexity and Predictability in Networks with Multiscale Entropy Analysis

Abstract

Abstract-We propose to use multiscale entropy analysis in characterisation of network traffic and spectrum usage. We show that with such analysis one can quantify complexity and predictability of measured traces in widely varying timescales. We also explicitly compare the results from entropy analysis to classical characterisations of scaling and self-similarity in time series by means of fractal dimension and the Hurst parameter. Our results show that the used entropy analysis indeed complements these measures, being able to uncover new information from traffic traces and time series models. We illustrate the application of these techniques both on time series models and on measured traffic traces of different types. As potential applications of entropy analysis in the networking area, we highlight and discuss anomaly detection and validation of traffic models. In particular, we show that anomalous network traffic can have significantly lower complexity than ordinary traffic, and that commonly used traffic and time series models have different entropy structures compared to the studied traffic traces. We also show that the entropy metrics can be applied to the analysis of wireless communication and networks. We point out that entropy metrics can improve the understanding of how spectrum usage changes over time and can be used to enhance the efficiency of dynamic spectrum access networks.

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

“…Similarly, Olivieri et al [123] have applied the information theoretic entropy as a measure of predictability in the process of generating the ON-and OFF-period durations. In [124]- [126], the authors have used state-of-the-art multiscale entropy metrics in order to examine the predictability of the spectrum measurement traces recorded as a function of the prediction complexity.…”

“…In [124]- [126], the authors have used the multi-scale entropy, in order to examine both the complexity and the predictability of the spectrum measurement traces recorded. In [127], from an information theory perspective, the authors have introduced a methodology of using statistical entropy measures and Fano inequality to quantify the degree of predictability underlying real-world spectrum measurements.…”

Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction

“…The traffic pattern knowledge discovery process can therefore be linked to the notion of entropy, which measures the degree of disorder in a system. Information Theory entropy is widely employed to predict human mobility, Asynchronous Transfer Mode (ATM) traffic streams and cellular network traffic [40]. Entropy clearly provides a measure of the extent to which the traffic can be predicted on the basis of the historical patterns over the area.…”

Predictability of Aggregated Traffic of Gateways in Wireless Mesh Network with AODV and DSDV Routing Protocols and RWP Mobility Model

“…However, the complexity of network traffic can be described in other ways, such as chaos [9][10][11] and the difficulty in predicting the future behavior of time series [12,13]. The predictability of network traffic can be used in traffic control and thus the network performance can be improved accordingly.…”

“…Costa et al [12,14] proposed a new complexity framework called multi-scale entropy (MSE) to analyze the predictability of time series across multiple time scales. Because network traffic is considered as time series, Riihijarvi et al [13,15] used multi-scale entropy method to analyze the predictability of the aggregated network traffic, too. In their papers, the complexity of wired and wireless traffic is analyzed and compared with fractal analysis results.…”