NDLI: A Time Series Approach for Inferring Orchestrated Probing Campaigns by Analyzing Darknet Traffic

Content Provider	IEEE Xplore Digital Library
Author	Bou-Harb, E. Debbabi, M. Assi, C.
Copyright Year	2015
Description	Author affiliation: NCFTA Canada, Concordia Univ., Montreal, QC, Canada (Bou-Harb, E.; Debbabi, M.; Assi, C.)
Abstract	This paper aims at inferring probing campaigns by investigating dark net traffic. The latter probing events refer to a new phenomenon of reconnaissance activities that are distinguished by their orchestration patterns. The objective is to provide a systematic methodology to infer, in a prompt manner, whether or not the perceived probing packets belong to an orchestrated campaign. Additionally, the methodology could be easily leveraged to generate network traffic signatures to facilitate capturing incoming packets as belonging to the same inferred campaign. Indeed, this would be utilized for early cyber attack warning and notification as well as for simplified analysis and tracking of such events. To realize such goals, the proposed approach models such challenging task as a problem of interpolating and predicting time series with missing values. By initially employing trigonometric interpolation and subsequently executing state space modeling in conjunction with a time-varying window algorithm, the proposed approach is able to pinpoint orchestrated probing campaigns by only monitoring few orchestrated flows. We empirically evaluate the effectiveness of the proposed model using 330 GB of real dark net data. By comparing the outcome with a previously validated work, the results indeed demonstrate the promptness and accuracy of the proposed approach.
Starting Page	180
Ending Page	185
File Size	150702
Page Count	6
File Format	PDF
e-ISBN	9781467365901
DOI	10.1109/ARES.2015.9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-24
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Interpolation Time series analysis Clustering algorithms Telescopes Internet IP networks Kalman filters
Content Type	Text
Resource Type	Article

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