An Error Reducing Structure for Restricting Jammers in Remote Networks

Authors(2) :-Gotthala Kodhanda Babu, B. Rama Subba Reddy

We aim to design a framework which will localize one or different jammers with a high accuracy. Most of the absolute jammer-localization schemes advance aberrant abstracts (e.g., audition ranges) afflicted by jam attacks, that makes it troublesome to localize jammers accurately. Instead, we tend to accomplishment an absolute altitude the backbone of jam signals (JSS). Estimating JSS is arduous as jam signals could also be anchored in additional signals. we tend to analyze many heuristics get algorithms for neighboring the well-rounded best resolution, and our simulation after-effects look that our error-minimizing-based framework achieves larger accomplishment than absolutely the schemes. Additionally, our error-minimizing framework will advance aberrant abstracts to access a much bigger space admiration compared with preceding work. we tend to show a multi-phase broadcast vulnerability detection, activity, and antibody different equipment alleged NICE, that is inherent in advance blueprint based mostly analytic models and recon?gurable basic network-based countermeasures. The projected framework leverages Open Flow arrangement programming arthropod genus to body an advisor and dominance even over broadcast programmable basic switches in adjustment to signi?cantly advance apprehension and abate advance consequences. The arrangement and aegis evaluations attest the potency and capability of the projected resolution.

Authors and Affiliations

Gotthala Kodhanda Babu
Department of Computer Science and Engineering, S.V.College of Engineering, Tirupati, India
B. Rama Subba Reddy
Department of Computer Science and Engineering, S.V.College of Engineering, Tirupati, India

Network Security, Cloud Computing, jam attacks.

  1. Coud Sercurity Alliance, “Top threats to cloud computing v1.0,”, March 2010.
  2. M. Armbrust, A. Fox, R. Grif?th, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A view of cloud computing,” ACM Commun., vol. 53, no. 4, pp. 50-58, Apr. 2010.
  3. B. Joshi, A. Vijayan, and B. Joshi, “Securing cloud computing environment against DDoS attacks,” IEEE Int'l Conf. Computer Communication and Informatics (ICCCI '12), Jan. 2012.
  4. 4H. Takabi, J. B. Joshi, and G. Ahn, “Security and privacy challenges in cloud computing environments,” IEEE Security & Privacy, vol. 8, no. 6, pp. 24-31, Dec. 2010.
  5. “Open vSwitch project,”, May 2012.
  6. Z. Duan, P. Chen, F. Sanchez, Y. Dong, M. Stephenson, and J. Barker, “Detecting spam zombies by monitoring outgoing messages,” IEEE Trans. Dependable and Secure Computing, vol. 9, no. 2, pp. 198-210, Apr. 2012.
  7. G. Gu, P. Porras, V. Yegneswaran, M. Fong, and W. Lee, “Bot Hunter: detecting malware infection through IDS-driven dialog correlation,” Proc. of 16th USENIX Security Symp. (SS '07), pp. 12:1-12:16, Aug. 2007.
  8. G. Gu, J. Zhang, and W. Lee, “BotSniffer: detecting botnet command and control channels in network traf?c,” Proc. of 15th Ann. Network and Distributed Sytem Security Symp. (NDSS '08), Feb. 2008.
  9. O. Sheyner, J. Haines, S. Jha, R. Lippmann, and J. M. Wing, “Automated generation and analysis of attack graphs,” Proc. IEEE Symp. on Security and Privacy, 2002, pp. 273-284.
  10. “NuSMV: A new symbolic model checker,” 1024/ nusmv. Aug. 2012.
  11. S. H. Ahmadinejad, S. Jalili, and M. Abadi, “A hybrid model for correlating alerts of known and unknown attack scenarios and updating attack graphs,” Computer Networks, vol. 55, no. 9, pp. 2221-2240, Jun. 2011.
  12. X. Ou, S. Govindavajhala, and A. W. Appel, “MulVAL: a logic based network security analyzer,” Proc. of 14th USENIX Security Symp., pp. 113-128. 2005.
  13. R. Sadoddin and A. Ghorbani, “Alert correlation survey: framework and techniques,” Proc. ACM Int'l Conf. on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services (PST '06), pp. 37:1-37:10. 2006.
  14. L. Wang, A. Liu, and S. Jajodia, “Using attack graphs for correlating, hypothesizing, and predicting intrusion alerts,” Computer Communications, vol. 29, no. 15, pp. 2917-2933, Sep. 2006.
  15. S. Roschke, F. Cheng, and C. Meinel, “A new alert correlation algorithm based on attack graph,” Computational Intelligence in Security for Information Systems, LNCS, vol. 6694, pp. 58-67. Springer, 2011.
  16. A. Roy, D. S. Kim, and K. Trivedi, “Scalable optimal countermeasure selection using implicit enumeration on attack countermeasure trees,” Proc. IEEE Int'l Conf. on Dependable Systems Networks (DSN '12), Jun. 2012.
  17. N. Poolsappasit, R. Dewri, and I. Ray, “Dynamic security risk management using bayesian attack graphs,” IEEE Trans. Dependable and Secure Computing, vol. 9, no. 1, pp. 61-74, Feb. 2012.
  18. Open Networking Fundation, “Software-denned networking: The new norm for networks,” ONF White Paper, Apr. 2012.
  19. “Open?ow.”, 2012.
  20. N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peter- son, J. Rexford, S. Shenker, and J. Turner, “OpenFlow: enabling innovation in campus networks,” SIGCOMM Comput. Commun. Rev., vol. 38, no. 2, pp. 69-74, Mar. 2008.
  21. E. Keller, J. Szefer, J. Rexford, and R. B. Lee, “NoHype: virtualized cloud infrastructure without the virtualization,” Proc. of the 37th ACM ann. int'l symp. on Computer architecture (ISCA '10), pp. 350-361. Jun. 2010.
  22. X. Ou, W. F. Boyer, and M. A. McQueen, “A scalable approach to attack graph generation,” Proc. of the 13th ACM conf. on Computer and communications security (CCS '06), pp. 336-345. 2006.
  23. Mitre Corporation, “Common vulnerabilities and exposures, CVE,” 2012.
  24. P. Mell, K. Scarfone, and S. Romanosky, “Common vulnerability scoring system(CVSS),”http://www.?, May 2010.
  25. O. Database, “Open source vulnerability database (OVSDB),” 2012
  26. NIST, “National vulnerability database, NVD,” http://nvd.nist. gov. 2012
  27. N. Gude, T. Koponen, J. Pettit, B. Pfaff, M. Casado, N. McKeown, and S. Shenker, “NOX: towards an operating system for networks,” SIGCOMM Comput. Commun. Rev., vol. 38, no. 3, pp.105-110, Jul. 2008.
  28. X. Ou and A. Singhal, Quantitative Security Risk Assessment of Enterprise Networks. Springer, Nov. 2011.
  29. M. Frigault and L. Wang, “Measuring network security using bayesian Network-Based attack graphs,” Proc. IEEE 32nd ann. int'l conf. on Computer Software and Applications (COMPSAC '08), pp. 698-703. Aug. 2008.
  30. K. Kwon, S. Ahn, and J. Chung, “Network security management using ARP spoo?ng,” Proc. Int'l Conf. on Computational Science and Its Applications (ICCSA '04), LNCS, vol. 3043, pp. 142-149, Springer, 2004.
  31. “Metasploit,” 2012.
  32. “Armitage,” 2012.
  33. M. Tupper and A. Zincir-Heywood, “VEA-bility security metric: A network security analysis tool,” Proc. IEEE Third Int'l Conf. on Availability, Reliability and Security (ARES '08), pp. 950-957, Mar. 2008.

Publication Details

Published in : Volume 3 | Issue 4 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 177-185
Manuscript Number : CSEIT1831437
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Gotthala Kodhanda Babu, B. Rama Subba Reddy, "An Error Reducing Structure for Restricting Jammers in Remote Networks", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.177-185, March-April-2018.
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