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A New Approach to Multivariate Network Traffic Analysis.

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Title: A New Approach to Multivariate Network Traffic Analysis.
Authors: Kim, Jinoh1,2 (AUTHOR) jinoh.kim@tamuc.edu, Sim, Alex2 (AUTHOR) asim@lbl.gov
Source: Journal of Computer Science & Technology (10009000). Mar2019, Vol. 34 Issue 2, p388-402. 15p.
Subjects: Anomaly detection (Computer security), Internet traffic, Multivariate analysis, Operations management, Computer network management
Abstract: Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: A New Approach to Multivariate Network Traffic Analysis.
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  Data: <searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+traffic%22">Internet traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+management%22">Operations management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+management%22">Computer network management</searchLink>
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  Data: Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11390-019-1915-y
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 388
    Subjects:
      – SubjectFull: Anomaly detection (Computer security)
        Type: general
      – SubjectFull: Internet traffic
        Type: general
      – SubjectFull: Multivariate analysis
        Type: general
      – SubjectFull: Operations management
        Type: general
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              Text: Mar2019
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              Y: 2019
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