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HTTP Botnet Defense Mechanism using System Dynamics based Genetic Algorithm

Affiliations

  • Department of Computer Science and Engineering, MVJ College of Engineering, Near ITPB, Whitefield, Bangalore-560 067, Karnataka, India

Abstract


Objectives: The system which is under the control of Bot master is called Bot. Botnet refers to the network of bots. Hypertext Transfer Protocol (HTTP) Botnet use HTTP protocol for communication. Findings: HTTP Botnet is difficult to detect since their features are somewhat similar to normal HTTP traffic1. Genetic algorithm Based detection method results in better analysis of botnet attacks. However, it sets the initialization pool by picking the values randomly and can assure only less false positive rate. Novelty: This paper proposes System Dynamics (SD) based Genetic Algorithm for improving the efficiency of Genetic algorithm and hence the botnet detection.

Keywords

Genetic Algorithm, HTTP Botnet, Layered Detection, System Dynamics.

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References


  • Koo TM, Chang HC, Wei GQ. Construction p2p firewall HTTP-botnet defence mechanis. Proceedings of IEEE International Conference on Computer Science and Automation Engineering, IEEE Xplore Digital Library; 2011 Jul 14.
  • Mathew SE, Ali A, Stephen J. Genetic algorithm based layered detection and defence of HTTP botnet. ACEEE International Journal on Network Security. 2014 Jan; 5(1).
  • Mathew SE, Ali A. Automated layered HTTP botnet defence mechanism. International Journal of Scientific and Engineering Research. 2013 Aug.
  • Abdullah B, Alghafar IA, Salama GI, Alhafez AA. Performance evaluation of a genetic algorithm based approach to network intrusion detection system. International Conference on Aerospace Sciences and Aviation Technology; 2009 May.
  • Bankovic Z, Stepanovic DA, Bojanic S, Taladriz ON. Improving network security using genetic algorithm approach. Computers and Electrical Engineering. 2007; 33:438–51.
  • Li W. A genetic algorithm approach to network intrusion detection. SANS Institute, USA; 2004.
  • Taylor BN, Kuyatt CE. Guidelines for evaluating and expressing the uncertainty of NIST measurement results. National Institute of Standards and Technology. 1994 Sep. p. 1–20.
  • Dallal GE. Degree of feedom [Internet]. 2007 [cited 2007 May]. Available from: h t t p : / / www.tufts.edu/-gdallaIldof.htmL.
  • NIST/SEMATECH, e-handbook of statistical methods; 2003 Jun.
  • Jones. Botnets: detection and mitigation. Federal Computer Incident Response Center (FEDCIRC); 2003 Feb.
  • Cooke E, Jahanian F, Pherson DC. The zombie roundup: understanding, detecting, and disturbing botnets. In the 1st workshop on steps to reducing unwanted traffic on the internet (SRUTI ’05); 2005 Jul.
  • Barford P, Yegneswaran V. An inside look at botnets. Special Workshop on Malware Detection, Advances in Information Security, Springer Verlag; 2006.
  • Choi H, Lee H, Lee H, Kim H. Botnet detection by monitoring group activities in DNS traffic. 7th IEEE International Conference on Computer and Information Technology (ICCIT); 2007. p. 715–20. Young M. The Technical Writer’s Handbook. Mill Valley, CA: University Science; 1989.
  • Mitchell M. An introduction to genetic algorithms. Massachusetts Institute of Technology (MIT) Press; 1996.
  • Goldberg DE, Smith RE. Nonstationary function optimization using genetic algorithms with diploidy and dominance. In Grefenstette JJ, editor, Proceedings of the Second International Conference on Genetic Algorithms. Lawrence Erlbaum Associates; 1987. p. 59–68.
  • Koza JR. Genetic programming: on the programming of computers by means of natural selection. MA:MIT Press; 1992; Cambridge.
  • Harik GR. (1995). Finding multimodal solutions using restricted tournament selection. In Eshelman LJ (ed.). Proceedings of the Sixth International Conference on Genetic Algorithms. 1995. p. 24–31; San Mateo. CA:Morgan Kaufmann Publishers.
  • Whitley D. The GENITOR algorithm and selection pressure. In Schaffer JD, editor. Proceedings of the Third International Conference on Genetic Algorithms; 1989. p. 161–21; San Mateo. Morgan Kaufmann; 1989.
  • Blickle D, Thiele L. A comparison of selection schemes used in genetic algorithm [Computer Engineering and Communication Networks Lab TIK thesis]. Gloriastresse 35, 8092 Zurich ,Switzerland, Swiss Federal Institute of Technology (ETH); 1995 Dec.
  • Sivanandam, Deepa SN. Introduction to genetic algorithms. Springer-Verlag Berlin Heidelberg; 2008.
  • Whitley D. (1988). GENITOR: a different genetic algorithm. In Proceedings of the Rocky Mountain Conference on Artificial Intelligence. Denver Colorado; 1988. p. 118–30.
  • Lee MA, Takagi H. Dynamic control of genetic algorithms using fuzzy logic techniques. Proceeding of 5th Internation Conference on Genetic Algorithms (ICGA’93), Urbana-Champaign, IL; 1993 Jul 17–21. p. 76–83.
  • Shan KH, Qing ZM, Jun T, Yuan LC. The research of simulation for network security based on system dynamics. Fifth International Conference on Information Assurance and Security; 2009.

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