Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 986428, 9 pages.

Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

Kuan-Cheng Lin, Sih-Yang Chen, and Jason C. Hung

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Abstract

Because of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms.

Article information

Source
J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 986428, 9 pages.

Dates
First available in Project Euclid: 1 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1412176992

Digital Object Identifier
doi:10.1155/2014/986428

Citation

Lin, Kuan-Cheng; Chen, Sih-Yang; Hung, Jason C. Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm. J. Appl. Math. 2014, Special Issue (2014), Article ID 986428, 9 pages. doi:10.1155/2014/986428. https://projecteuclid.org/euclid.jam/1412176992


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References

  • L. Atzori, A. Iera, and G. Morabito, “The internet of things: a survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
  • Y. Pan and J. Zhang, “Parallel programming on cloud computing platforms–-challenges and solutions,” Journal of Convergence, vol. 3, no. 4, pp. 23–28, 2012.
  • K. Wang, C.-Y. Huang, S.-J. Lin, and Y.-D. Lin, “A fuzzy pattern-based filtering algorithm for botnet detection,” Computer Networks, vol. 55, no. 15, pp. 3275–3286, 2011.
  • H. Choi and H. Lee, “Identifying botnets by capturing group activities in DNS traffic,” Computer Networks, vol. 56, no. 1, pp. 20–33, 2012.
  • W. T. Strayer, D. Lapsely, R. Walsh, and C. Livadas, “Botnet detection based on network behavior,” Advances in Information Security, vol. 36, pp. 1–24, 2008.
  • M. Abu Rajab, J. Zarfoss, F. Monrose, and A. Terzis, “A multifaceted approach to understanding the botnet phenomenon,” in Proceedings of the 6th ACM SIGCOMM on Internet Measurement Conference (IMC '06), pp. 41–52, October 2006.
  • M. S. Obaidat and F. Zarai, “Novel algorithm for secured mobility and IP traceability for WLAN networks,” Journal of Convergence, vol. 3, no. 2, pp. 1–8, 2012.
  • M. Feily, A. Shahrestani, and S. Ramadass, “A survey of botnet and botnet detection,” in Proceedings of the 3rd International Conference on Emerging Security Information, Systems and Technologies (SECURWARE '09), pp. 268–273, June 2009.
  • R. Pan, G. Xu, B. Fu, P. Dolog, Z. Wang, and M. Leginus, “Improving recommendations by the clustering of tag neighbours,” Journal of Convergence, vol. 3, no. 1, pp. 13–20, 2012.
  • A. Bhattacharya, W. Wu, and Z. Yang, “Quality of experience evaluation of voice communication: an affect-based approach,” Human-Centric Computing and Information Sciences, vol. 2, article 7, 2012.
  • J. R. Quinlan, C4.5: Programs for Machine Learning, The Morgan Kaufmann Series in Machine Learning, Morgan Kaufmann Publishers, San Mateo, Calif, USA, 1993.
  • T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, “Support vector machine classification and validation of cancer tissue samples using microarray expression data,” Bioinformatics, vol. 16, no. 10, pp. 906–914, 2000.
  • M. Abdel Fattah, “The use of MSVM and HMM for sentence alignment,” Journal of Information Processing Systems, vol. 8, no. 2, 2012.
  • G. P. Zhang, “Neural networks for classification: a survey,” IEEE Transactions on Systems, Man and Cybernetics C: Applications and Reviews, vol. 30, no. 4, pp. 451–462, 2000.
  • K. Sarkar, M. Nasipuri, and S. Ghose, “Machine learning based keyphrase extraction: comparing decision trees, naïve Bayes, and artificial neural networks,” Journal of Information Processing Systems, vol. 8, no. 4, pp. 693–712, 2012.
  • M. Dash and H. Liu, “Feature selection for classification,” Intelligent Data Analysis, vol. 1, no. 1–4, pp. 131–156, 1997.
  • A. James and S. Dimitrijev, “Ranked selection of nearest discriminating features,” Human-Centric Computing and Information Sciences, vol. 2, article 12, 2012.
  • S. Farzi, “Efficient job scheduling in grid computing with modified artificial fish swarm algorithm,” International Journal of Computer Theory and Engineering, vol. 1, no. 1, pp. 13–18, 2009.
  • C.-L. Huang and C.-J. Wang, “A GA-based feature selection and parameters optimizationfor support vector machines,” Expert Systems with Applications, vol. 31, no. 2, pp. 231–240, 2006.
  • J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, December 1995.
  • B. Singh and D. Lobiyal, “A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks,” Human-Centric Computing and Information Sciences, vol. 2, article 13, 2012.
  • K.-C. Lin and H.-Y. Chien, “CSO-based feature selection and parameter optimization for support vector machine,” in Proceedings of the Joint Conferences on Pervasive Computing (JCPC '09), pp. 783–788, December 2009.
  • M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 26, no. 1, pp. 29–41, 1996.
  • X.-L. Li, Z.-J. Shao, and J.-X. Qian, “Optimizing method based on autonomous animats: fish-swarm Algorithm,” System Engineering Theory and Practice, vol. 22, no. 11, pp. 32–38, 2002.
  • H. Chen, S. Wang, J. Li, and Y. Li, “A hybrid of artificial fish swarm algorithm and particle swarm optimization for feedforward neural network training,” in Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering, 2007.
  • J. L. Liao and K. C. Lin, A Study of Feature Selection Integrated with Back-Propagation Network for Botnet Detection, National Chung Hsing University, Taichung, Taiwan, 2013.
  • C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
  • V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  • R. Malhotra and A. Jain, “Fault prediction using statistical and machine learning methods for improving software quality,” Journal of Information Processing Systems, vol. 8, no. 2, pp. 241–262, 2012.
  • C. Langin, H. Zhou, S. Rahimi, B. Gupta, M. Zargham, and M. R. Sayeh, “A self-organizing map and its modeling for discovering malignant network traffic,” in Proceedings of the IEEE Symposium on Computational Intelligence in Cyber Security (CICS '09), pp. 122–129, Nashville, Tenn, USA, April 2009.
  • T. Liu, Y.-B. Hou, A.-L. Qi, and X.-T. Chang, “Feature optimization based on Artificial Fish-swarm Algorithm in intrusion detections,” in Proceedings of the International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC '09), pp. 542–545, April 2009.
  • C. C. Chang and C. J. Lin, “LIBSVM: A Library for Support Vector Machines,” http://www.csie.ntu.edu.tw/$\sim\,\!$cjlin/libsvm/. \endinput