Finding Misconfigurations in Router and Network Using Minerals

International Journal of Computer Science (IJCS Journal) Published by SK Research Group of Companies (SKRGC) Scholarly Peer Reviewed Research Journals

Format: Volume 1, Issue 2, No 1, 2013.

Copyright: All Rights Reserved ©2013

Year of Publication: 2013

Author: S.Arockia Rubi,M.Brindha Devi


View PDF Format


The router misconfigurations are common and can have dramatic consequences to the operation of the network. Misconfigurations can compromise the security of an entire network or even cause disruption to the Internet connectivity. The solutions have been proposed can detect a number of problems in real configurations files. These solutions share a common limitation, based on rules which need to be known beforehand. In order to overcome these limitations, address the problem of router misconfigurations using data mining technique. More specifically, minerals using association rule mining. While association rule mining has traditionally applied to the configuration files of routers across an administrative domain to discover local, network specific policies. Deviations from these local policies are potential misconfigurations. The configuration files are user accounts, interfaces and BGP sessions.


[1] El-Arini, K. and Killourhy, K. (2005) ‗Bayesian Detection of Router Configuration Anomalies‗, presented at the ACM SIGCOMM Workshop on Mining Network Data (MineNet‗05), Philadelphia, PA. [2] Wool, A. (2004) ‗A Quantitative Study of Firewall Configuration Errors‗, IEEE Computer, vol. 37, no. 6, pp. 62–67. [3] Caldwell, D. Gilbert, A. Gottlieb, J. Greenberg, A. Hjalmtysson, G. and Rexford, J. (2003) ‗The Cutting EDGE of IP Router Configuration,‗ presented at the ACM SIGCOMM HotNets-II Workshop, Cambridge, MA. [4] Mahajan, R. Wetherall, D. and Anderson, T. (2002) ‗Understanding BGP Misconfiguration‗, in Proc. ACM SIGCOMM, Pittsburgh, PA, pp. 3–16.5. [5] Feldmann, A. and Rexford, J. (2001) ‗IP Network Configuration for Intradomain Traffic Engineering‗ IEEE Network, vol. 15, no. 5, pp. 46–57.


Association rules mining, error detection, network management, static analysis.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.   

Facebook IconYouTube IconTwitter IconVisit Our Blog