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Comparative Evaluation of Algorithm based Approach for Intrusion Detection using a Hybrid Model

by Shardul S. Mahadik, Sukanya S. Parsekar, Sushant B. Choudhary, Pallavi S. Kulkarni
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 14
Year of Publication: 2015
Authors: Shardul S. Mahadik, Sukanya S. Parsekar, Sushant B. Choudhary, Pallavi S. Kulkarni
10.5120/19891-1971

Shardul S. Mahadik, Sukanya S. Parsekar, Sushant B. Choudhary, Pallavi S. Kulkarni . Comparative Evaluation of Algorithm based Approach for Intrusion Detection using a Hybrid Model. International Journal of Computer Applications. 113, 14 ( March 2015), 1-4. DOI=10.5120/19891-1971

@article{ 10.5120/19891-1971,
author = { Shardul S. Mahadik, Sukanya S. Parsekar, Sushant B. Choudhary, Pallavi S. Kulkarni },
title = { Comparative Evaluation of Algorithm based Approach for Intrusion Detection using a Hybrid Model },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 14 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number14/19891-1971/ },
doi = { 10.5120/19891-1971 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:54.163210+05:30
%A Shardul S. Mahadik
%A Sukanya S. Parsekar
%A Sushant B. Choudhary
%A Pallavi S. Kulkarni
%T Comparative Evaluation of Algorithm based Approach for Intrusion Detection using a Hybrid Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 14
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Adequate system security is the first step towards data integrity and protection, however even with the most advanced protection, modern computer and communication infrastructures are susceptible to various types of attacks. With traditional signature based systems losing proficiency, the Hybrid Intrusion Detection System (HIDS) approach proves the vitality of detecting intrusions and anomalies, simultaneously, by automated data mining over network traffic and signature generation. This paper will focus on analyzing different anomaly detection techniques used to detect zero day attacks and an automatic attack signature generation mechanism that can be complemented with the former. This will serve to be an elemental analysis of a few techniques, their working, and their pros and cons put together in a concise form.

References
  1. K. Hwang, M. Cai, Y. Chen, and M. Qin, "Hybrid intrusion detection with weighted signature generation over anomalous internet episodes," Dependable and Secure Computing, IEEE Transactions on, vol. 4, no. 1, pp. 41–55, 2007.
  2. B. Caswell and J. Beale, Snort 2. 1 intrusion detection. Syngress, 2004.
  3. M. Roesch et al. , "Snort: Lightweight intrusion detection for networks. ," in LISA, vol. 99, pp. 229–238, 1999.
  4. Y. -J. Lee, Y. -R. Yeh, and Y. -C. F. Wang, "Anomaly detection via online oversampling principal component analysis," Knowledge and Data Engineering, IEEE Transactions on, vol. 25, no. 7, pp. 1460–1470, 2013.
  5. A. G. Tartakovsky, A. S. Polunchenko, and G. Sokolov, "Efficient computer network anomaly detection by changepoint detection methods," Selected Topics in Signal Processing, IEEE Journal of, vol. 7, no. 1, pp. 4–11, 2013.
  6. A. Shabtai, E. Menahem, and Y. Elovici, "F-sign: Automatic, function-based signature generation for malware," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 41, no. 4, pp. 494–508, 2011.
  7. V. Barnett and T. Lewis, Outliers in statistical data, vol. 3. Wiley New York, 1994.
  8. M. M. Breunig, H. -P. Kriegel, R. T. Ng, and J. Sander, "Lof: identifying density-based local outliers," in ACM sigmod record, vol. 29, pp. 93–104, ACM, 2000.
  9. D. M. Hawkins, Identification of outliers, vol. 11. Springer, 1980.
  10. W. Jin, A. K. Tung, J. Han, and W. Wang, "Ranking outliers using symmetric neighborhood relationship," in Advances in Knowledge Discovery and Data Mining, pp. 577–593, Springer, 2006.
  11. N. L. D. Khoa and S. Chawla, "Robust outlier detection using commute time and eigenspace embedding," in Advances in Knowledge Discovery and Data Mining, pp. 422–434, Springer, 2010.
  12. E. M. Knox and R. T. Ng, "Algorithms for mining distancebased outliers in large datasets," in Proceedings of the International Conference on Very Large Data Bases, pp. 392–403, Citeseer, 1998.
  13. Y. -J. Lee, Y. -R. Yeh, and Y. -C. F. Wang, "Anomaly detection via oversampling principal component analysis," in New Advances in Intelligent Decision Technologies, pp. 449–458, Springer, 1998.
  14. S. Roberts, "A comparison of some control chart procedures," Technometrics, vol. 8, no. 3, pp. 411–430, 1966.
  15. A. N. Shiryaev, "The problem of the most rapid detection of a disturbance in a stationary process," Soviet Math. Dokl. , vol. 2, pp. 795–799, 1961.
  16. A. N. Shiryaev, "On optimum methods in quickest detection problems," Theory of Probability and its Applications, vol. 8, no. 1, pp. 22–46, 1964.
Index Terms

Computer Science
Information Sciences

Keywords

Hybrid intrusion detection system online oversampling principal component analysis change point detection shiryaev roberts f-sign automatic signature generation

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