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Reseach Article

A Survey on different types of Intrusion Detection Systems

by Mayur V. Suramwar, Bansode S.m
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 16
Year of Publication: 2015
Authors: Mayur V. Suramwar, Bansode S.m
10.5120/21788-5097

Mayur V. Suramwar, Bansode S.m . A Survey on different types of Intrusion Detection Systems. International Journal of Computer Applications. 122, 16 ( July 2015), 34-38. DOI=10.5120/21788-5097

@article{ 10.5120/21788-5097,
author = { Mayur V. Suramwar, Bansode S.m },
title = { A Survey on different types of Intrusion Detection Systems },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 16 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number16/21788-5097/ },
doi = { 10.5120/21788-5097 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:46.021861+05:30
%A Mayur V. Suramwar
%A Bansode S.m
%T A Survey on different types of Intrusion Detection Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 16
%P 34-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Modern network systems have abundant trouble in security vulnerabilities like buffer overflow, bugs in Microsoft web, SQL injection, security of applications and operating systems, Sniffer Attack. Also, wireless devices mostly personal computers, sensors, personal digital assistants, and smart phones became economically doable as a result of advances in communication and manufacturing of small sensors. There are many kinds of different vulnerabilities to be exploited in such types of devices. Therefore to enhance different kind of securities, many kinds of mechanism are developed such as access control, cryptography, authentication, and many intrusion detection systems. Intrusion detection methods broadly organized into following two different types: one is anomaly detection and other one is misuse detection. Anomaly detection provides number of ways to try and verify whether the deviation is from the confirmed traditional usage patterns or not. The crucial fortune of anomaly detection lean on the expected pattern behaviors. Also, misuse detection system use different types of attacks which are known or different inadequate spots of the different systems to verify intrusions. The weakness of misuse detection system is not able to find any upcoming (unknown) intrusion until the system does not know the corresponding attack signatures.

References
  1. The Bro Network Security Monitor. [Online]. Available: http://bro-ids. org.
  2. Network Flight Recorder. [Online]. Available: http://www. checkpoint. com/ corporate/nfr/index. html.
  3. X. A. Dimitropoulos and G. F. Riley, "Creating realistic BGP models," in Proc. of the 11th IEEE/ACM Int. Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, Orlando, 2003, pp. 64–70.
  4. M. Le, A. Stavrou, and B. B. H. Kang, "Double guard: detecting intrusions in multitier web applications," IEEE Trans. on Dependable and Secure Computer, vol. 9, no. 4, pp. 512–525, 2012.
  5. Y. -J. Lee, Y. -R. Yeh, and Y. -C. F. Wang, "Anomaly detection via online over-sampling principal component analysis," IEEE Trans. on Knowledge and Data Engineering, doi: 09/TKDE. 2012. 99, 2012.
  6. M. Mohajerani, A. Moeini, and M. Kianie, "NFIDS: A neuro-fuzzy intrusion detection system," in Proc. of the 10th IEEE Int. Conf. on Electronics, Circuits and Systems, Sharjah, 2003, pp. 348–351.
  7. Y. Wang, W. Fu, and D. P. Agrawal, "Gaussian versus uniform distribution for intrusion detection in wireless sensor networks," IEEE Trans. on Parallel and Distributed Systems, doi: 09/TPDS. 2012. 105, 2012.
  8. K. Ilgun, R. A. Kemmerer, and P. A. Porras, "State transition analysis: a rule-based intrusion detection approach," IEEE Trans. on Software Engineering, vol. 21, no. 3, pp. 181–199, 1995.
  9. C. -C. Lee, M. -S. Hwang, and W. -P. Yang, "Extension of authentication protocol for GSM," IEE Proc. — Communications, vol. 150, no. 2, pp. 91–95, Apr. 2003.
  10. H. -Y. Lin and W. -G. Tzeng, "A secure erasure code based cloud storage system with secure data forwarding," IEEE Trans. on Parallel and Distributed Systems, vol. 23, no. 6, pp. 995–1003, 2012.
  11. R. Sanchez, F. Almenares, P. Arias, D. Diaz-Sanchez, and A. Marin, "Enhancing privacy and dynamic federation in IdM for consumer cloud computing," IEEE Trans. on Consumer Electronics, vol. 58, no. 1, pp. 95–103, 2012.
  12. F. Wang, Y. Zhang, and J. Ma, "Modelling and analyzing passive worms over unstructured peer-to-peer networks," Int. Journal of Network Security, vol. 11, no. 1, pp. 39–45, 2010.
  13. C. -Y. Ho, Y. -C. Lai, I-W. Chen, F. -Y. Wang, and W. -H. Tai, "Statistical analysis of false positives and faluse negatives from real traffic with intrusion detection/prevention systems," IEEE Communications Magazine, vol. 50, no. 3, pp. 146–154, 2012.
  14. M. Mohajerani, A. Moeini, and M. Kianie, "NFIDS: A neuro-fuzzy intrusion detection system," in Proc. of the 10th IEEE Int. Conf. on Electronics, Circuits and Systems, Sharjah, 2003, pp. 348–351.
Index Terms

Computer Science
Information Sciences

Keywords

Security challenges threat and countermeasures anomaly detection intrusion detection systems misuse detection.