NDLI: On the combined use of sketches and CUSUM for Anomaly Detection

Content Provider	IEEE Xplore Digital Library
Author	Callegari, Christian Giordano, Stefano Pagano, Michele
Copyright Year	2015
Description	Author affiliation: CNIT and Dept. of Information Engineering, University of Pisa, Italy (Callegari, Christian; Giordano, Stefano; Pagano, Michele)
Abstract	Anomaly-based Intrusion Detection is a key research topic in network security due to its ability to face unknown attacks and new security threats. Moreover, new solutions should cope with scalability issues derived from the growth of the Internet traffic. To this aim random aggregation through the use of sketches represents a powerful prefiltering stage that can be applied to backbone data traffic with a performance improvement wrt traditional static aggregations at subnet level. In the paper we apply the CUSUM algorithm at the bucket level to reveal the presence of anomalies in the current data and, in order to improve the detection rate, we correlate the data corresponding to traffic flows aggregation based on different fields of the network and transport level headers. As a side effect, the correlation procedure gives some hints on the typology of the intrusions since different attacks determine the variability of the statistics associated to specific header fields. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed approach, confirming the goodness of CUSUM as a change-point detection algorithm.
Starting Page	157
Ending Page	162
File Size	522936
Page Count	6
File Format	PDF
e-ISBN	9781467373098
DOI	10.1109/CoCoNet.2015.7411181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Change detection algorithms Ports (Computers) Internet IP networks Computer crime Monitoring
Content Type	Text
Resource Type	Article

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