By Antonio Cuadra-Sánchez, Javier Aracil
This e-book offers an outline of site visitors anomaly detection research, permitting you to observe defense features of multimedia prone. The author's procedure relies at the research of time aggregation adjoining classes of the site visitors. As site visitors varies through the day, it truly is necessary to contemplate the concrete site visitors interval within which the ambiguity happens. This booklet offers the algorithms proposed in particular for this research and an empirical comparative research of these equipment and settle a brand new info idea dependent strategy, named "typical day analysis".
- A new information-theory established procedure for site visitors anomaly detection (typical day analysis)
- Introductory chapters to anomaly detection equipment together with regulate charts, assessments of goodness-of-fit Mutual Information
- Contains comparative research of site visitors anomaly detection methods
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Extra resources for Traffic Anomaly Detection
2. Trafﬁc proﬁle of the 39 monitored working days. zip Therefore, we inject trafﬁc on those three intervals in terms of 40% higher than the maximum and 40% lower than the minimum of the entire series, and 40% higher than the maximum average and 40% lower than the minimum average of that period. Thus, we compare the values of each algorithm before and after adding the contaminated trafﬁc. 3 shows the trafﬁc contaminated evenhandedly and weighed on the 39 day baseline scenario (in gray). 4. Proposed change point detection algorithms In this section, we gather the algorithms tested in this chapter: the most relevant statistical control chart (CUSUM), the two main tests of goodness-of-ﬁt (Pearson’s Chi-squared Comparative Analysis of Trafﬁc Anomaly Detection Methods 37 and Kolmogorov-Smirnov (K-S) tests) and MI.
5. Acknowledgments This work is carried out within the EUREKA Celtic-plus project Next generation Over-The-Top multimedia services (NOTTS) and co-funded by the Spanish public authority CDTI. , L UCAS C. , “Mutual information-based feature selection for intrusion detection systems”, Journal of Network and Computer Applications, Elsevier, vol. 34, no. 4, pp. 1184–1199, 2011. , “Anomaly detection in computer networks: a state-of-the-art review”, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), vol.
Continuous inspection schemes”, Biometrika, vol. 41, no. 1/2, pp. 100–115, June 1954. -L. , “Longterm forecasting of Internet backbone trafﬁc”, IEEE Transactions on Neural Networks, IEEE, vol. 16, no. 5, pp. 1110–1124, 2005. , “End-to-end routing behavior in the Internet”, ACM SIGCOMM Computer Communication Review, ACM, vol. 26, pp. 25–38, 1996. , “On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling”, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Taylor & Francis, vol.