Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2014, Article ID 197961, 8 pages
Research Article

Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures

Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia

Received 8 January 2014; Revised 13 March 2014; Accepted 15 March 2014; Published 15 April 2014

Academic Editor: Sebastian Ventura

Copyright © 2014 Ismahani Ismail 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. G. Varghese, J. A. Fingerhut, and F. Bonomi, “Detecting evasion attacks at high speeds without reassembly,” in Proceedings of the SIGCOMM Conference, pp. 327–338, Pisa, Italy, 2006.
  2. E. P. Markatos, “Speeding up TCP/IP: faster processors are not enough,” in Proceedings of the 21st IEEE International Performance, Computing, and Communications Conference (IPCCC '02), pp. 341–345, Phoenix, Ariz, USA, April 2002. View at Scopus
  3. P. Inella, An Introduction to Intrusion IDS, 2001,
  4. N. Desai, Intrusion Prevention Systems: the Next Step in the Evolution of IDS, 2003,
  5. J. Zico Kolter and M. A. Maloof, “Learning to detect and classify malicious executables in the wild,” Journal of Machine Learning Research, vol. 7, pp. 2721–2744, 2006. View at Google Scholar · View at Scopus
  6. R. Moskovitch, D. Stopel, C. Feher, N. Nissim, and Y. Elovici, “Unknown malcode detection via text categorization and the imbalance problem,” in Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 156–161, Taiwan, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Roesch, Snort, 2001,
  8. T. H. Ptacek and T. N. Newsham, “Insertion, evasion, and denial of service: eluding network intrusion detection,” Tech. Rep. T2R-0Y6, Calgary, Canada, 1998. View at Google Scholar
  9. M. Z. Shafiq, S. A. Khayam, and M. Farooq, “Improving accuracy of immune-inspired malware detectors by using intelligent features,” in Proceedings of the 10th Annual Genetic and Evolutionary Computation Conference (GECCO '08), pp. 119–126, Atlanta, Ga, usa, July 2008. View at Scopus
  10. C. Sarkar, Connection Establishment in TCP Three Way Handshaking, M. Tech—I, CSE IIT Bombay, 2009.
  11. T. Abou-Assaleh, N. Cercone, V. Keselj, and R. Sweidan, “Detection of new malicious code using N-grams signatures,” in Proceedings of the 2nd Annual Conference on Privacy, Security and Trust, pp. 193–196, Fredericton, NB, Canada.
  12. Y. Yang and J. A. Pedersen, “Comparative study on feature selection in text categorization,” in Proceedings of the 14th International Conference on Machine Learning, pp. 412–420.
  13. I. Ismail, M. N. Marsono, and S. M. Nor, “Detecting worms using data mining techniques : learning in the presence of class noise,” in Proceedings of the 6th International Conference on Signal Image Technology and Internet Based Systems (SITIS '10), pp. 187–194, Kuala Lumpur, Malaysia, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. A. McCalum and K. A. Nigam, “Comparison of event models for naive bayes text classification,” in Proceedings of the 15th National Conference on Artificial Intelligence (AAAI '98), pp. 41–48, Madison, Wis, USA, 1998.
  15. L. M. Garcia, Tcpdump and Libpcap, 2010,
  16. L. Zeltser, “Understanding Anti-Virus Software,” The Monthly Security Awareness Newsletter for Computer Users, The SANS Institute, 2011.
  17. P. Simonea, “The OSI Model: understanding the seven layers of computer networks,” Expert Reference Series of White Papers, Global Knowledge, 2006.
  18. C. Fosnock, “Computer worms: past, present and future,” CISSP, MCSE, CNE East Carolina University, 2005.