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Security and Communication Networks
Volume 2018 (2018), Article ID 2063089, 10 pages
https://doi.org/10.1155/2018/2063089
Research Article

A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

1Institute of Information Technology and Network Security, People’s Public Security University of China, Beijing, China
2Collaborative Innovation Center of Security and Law for Cyberspace, Beijing, China

Correspondence should be addressed to Tianliang Lu; moc.621@531ltl

Received 21 October 2017; Accepted 31 January 2018; Published 28 February 2018

Academic Editor: Paolo D'Arco

Copyright © 2018 Tianliang Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Rapid7, “Metasploit: the world’s most used penetration testing framework,” 2017, https://www.metasploit.com/.
  2. A. Basu, A. Mathuria, and N. Chowdary, Automatic Generation of Compact Alphanumeric Shellcodes for X86, vol. 8880 of Lecture Notes in Computer Science, Springer, Berlin, Germany, 2014. View at Scopus
  3. N. Verma, V. Mishra, and V. P. Singh, “Detection of alphanumeric shellcodes using similarity index,” in Proceedings of the 3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, pp. 1573–1577, India, September 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Zhao and G.-J. Ahn, “Using instruction sequence abstraction for shellcode detection and attribution,” in Proceedings of the 1st IEEE International Conference on Communications and Network Security, CNS 2013, pp. 323–331, USA, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Lukan, “Shellcode detection and emulation with libemu,” 2014, http://resources.infosecinstitute.com/shellcode-detection-emulation-libemu/.
  6. M. Polychronakis, K. G. Anagnostakis, and E. P. Markatos, “Network-level polymorphic shellcode detection using emulation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 4064, pp. 54–73, 2006. View at Google Scholar · View at Scopus
  7. Y. Luo, C. Xia, and Y. Li, “A Polymorphic Shellcode Detection Method Based on Dual-Mode Virtual Machine,” Jourmal of Computer Research and Development, vol. 51, no. 8, pp. 1704–1714, 2014. View at Google Scholar
  8. B. Gu, X. Bai, Z. Yang, A. C. Champion, and D. Xuan, “Malicious shellcode detection with virtual memory snapshots,” in Proceedings of the IEEE INFOCOM 2010, USA, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Dasgupta, S. Yu, and F. Nino, “Recent advances in artificial immune systems: models and applications,” Applied Soft Computing, vol. 11, no. 2, pp. 1574–1587, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Tan, “Artificial Immune System: Applications in Computer Security,” Artificial Immune System: Applications in Computer Security, pp. 1–174, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Gong and B. Bhargava, “Immunizing mobile ad hoc networks against collaborative attacks using cooperative immune model,” Security and Communication Networks, vol. 6, no. 1, pp. 58–68, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Brown, M. Anwar, and G. Dozier, “Detection of Mobile Malware: an Artificial Immunity Approach,” in Proceedings of the 2016 IEEE Symposium on Security and Privacy Workshops, SPW 2016, pp. 74–80, usa, May 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Singh, “Artificial Immune System Based Statistical Model for Intrusion Identification,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 6, pp. 170–176, 2015. View at Google Scholar
  14. I. Idris, A. Selamat, and S. Omatu, “Hybrid email spam detection model with negative selection algorithm and differential evolution,” Engineering Applications of Artificial Intelligence, vol. 28, pp. 97–110, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Forrest, L. Allen, A. S. Perelson, and R. Cherukuri, “Self-nonself discrimination in a computer,” in Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 202–212, May 1994. View at Scopus
  16. F. A. Gonzalez and D. Dasgupta, “Anomaly detection using real-valued negative selection,” Genetic Programming and Evolvable Machines, vol. 4, no. 4, pp. 383–403, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. Ji and D. Dasgupta, “Real-valued negative selection algorithm with variable-sized detectors,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 3102, pp. 287–298, 2004. View at Google Scholar · View at Scopus
  18. J. M. Shapiro, G. B. Lament, and G. L. Peterson, “An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection,” in Proceedings of the GECCO 2005 - Genetic and Evolutionary Computation Conference, pp. 337–344, usa, June 2005. View at Scopus
  19. L. N. de Castro and F. J. von Zuben, “Learning and optimization using the clonal selection principle,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 3, pp. 239–251, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Zimmer, “Scdbg is a shellcode analysis application built around the libemu emulation library,” 2011, http://sandsprite.com/blogs/index.php?uid=7&pid=152.