By Antonio Cuadra-Sánchez, Javier Aracil
This publication provides an outline of site visitors anomaly detection research, permitting you to watch safeguard facets of multimedia providers. The author's method is predicated at the research of time aggregation adjoining classes of the site visitors. As site visitors varies in the course of the day, it really is necessary to ponder the concrete site visitors interval within which the paradox happens. This ebook provides the algorithms proposed in particular for this research and an empirical comparative research of these tools and settle a brand new details thought dependent process, named "typical day analysis".
- A new information-theory established method for site visitors anomaly detection (typical day analysis)
- Introductory chapters to anomaly detection tools together with keep watch over charts, exams of goodness-of-fit Mutual Information
- Contains comparative research of site visitors anomaly detection methods
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Additional info for Traffic Anomaly Detection
3. Trafﬁc contaminated evenhandedly and weighed. 1. Statistical control charts The state of the art shows that there is no single SCC algorithm that stands out from the others, but the performance of SCC depends on the underlying trafﬁc characteristics [MAR 11, MAT 11, CAR 12, OPR 13, BUL 12]. g. EWMA) would lead to equivalent results. 38 Trafﬁc Anomaly Detection Control charts perform measurements obtained as variations from the expected value by the standard deviation. In particular, CUSUM (CUMulative SUM) is a sequential analysis algorithm that allows us to monitor sudden changes in continuous processes, such as changes in trafﬁc.
They present a fault diagnosis procedure based on discriminant analysis and MI. In order to obtain good classiﬁcation performances, a selection of important features is done with a newly developed algorithm based on the MI between variables. Nevertheless, they do not use the MI theory as a criterion for fault detection. In the same way, in [AMI 11], the authors present MI-based feature selection for intrusion detection systems. The usefulness of MI for selecting the most relevant features in a given classiﬁcation task has been proven in other ﬁelds [DRU 14].
5) differs from the ﬁrst scenario in that the increased trafﬁc in those periods is not the same but 40% higher than the maximum average inside each period (75 KBytes/s at 4am, 425 KBytes/s at 12am and 350 KBytes/s at 5pm). MI detects the last two periods (20 and 40%), CUSUM detects the intermediate period (100% increase) and χ2 detects the ﬁrst period (30% higher). 6), we have notably decreased the trafﬁc 40% lower than the minimum of the entire series for the three periods evenhandedly (1 KByte/s).