ОЦЕНКА НА ЕФЕКТИВНОСТТА НА БАЗАТА НА АНОМАЛНИ ИДЕНТИФИКАЦИИ ОТНОСНО АЛГОРИТЪТ ЗА КЛЪСТЕРИИ И ДИНАМИКАТА НА ДАННИ ТЕХНИКИ
Ключови думи :
аномалии базирани IDS, 2-озн. групиране, дърво за класификация, Wagner-Фишер разстояние, Индекс на Jaccard, Индекс Davies-Bouldin, Индекс Dunn, C-индексАбстракт
Целта на тази статия е да се проучи възможността за подход, базиран на клъстеризация, на базирани на аномалии системи за откриване на проникване (IDS). Изследваната методология включва алгоритъм за клъстериране с 2 средства с и без техники за извличане на данни, т.е. класификационни дървета. С цел да се оцени ефективността на методологията, бе приложен индексът Jaccard. Индексът Davies-Bouldin, индексът Dunn и индексът C бяха приложени, за да се сравнят резултатите от изпълнението на двата модела.
Сваляния
Данните за свалянията все още не са налични.
Литература (библиография)
1. Dagorn N., WebIDS: A Cooperative Bayesian Anomaly-Based Intrusion Detection System for Web Applications, Recent Advances in Intrusion Detection, LNCS, Vol. 5230/2008, Springer Berlin / Heidelberg, pp. 392-393.
2. Davies, D.L., Bouldin, D.W., (2000) A cluster separation measure, IEEE Trans. Pattern Anal. Machine Intell., 1(4), 1979, 224-227.
3. Dunn, J. (1974) Well separated clusters and optimal fuzzy partitions, Journal of Cybernetics , 4, 1974, 95-104.
4. Gorodetsky, V., Karsaeyv, O., Samoilov, V., Multi-agent technology for distributed data mining and classification, In Proceedings of IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003, pp. 438- 441.
5. Hubert L, Schultz J. Quadratic assignment as a general data-analysis strategy . British Journal of Mathematical and Statistical Psychologie, 1976; 190-241.
6. Ferri C., N. Lachinche, S. A. Macskassy, A. Rakotomamonjy, Second Workshop on ROC
Analysis in ML, 2005.
7. Forrest S., S.A. Hofmeyr, A. Somayaji, T.A. Longtaff, A Sense of Self for Unix Processes, In Proceedings of the 1996 IEEE Symposium on Security and Privacy, IEEE Computer Society Press, Los Alamitors, CA, pp.120-128.
8. Forrest S., S.A. Hofmeyr, A. Somayaji, Intrusion detection using sequences of system calls, Journal of Computer Security, Vol. 6, 1998, pp. 151-180.
9. Jaccard P., Distribution de la florine alpine dans la Bassin de Dranses et dans quelques regions voisines, Bulletin de la Societe Vaudoise des Sciences Naturelles, 37, 1901, 241-272.
10. Jyothsna V., V. V. Rama Prasad, K. Munivara Prasad, A Review of Anomaly based Intrusion Detection Systems, International Journal of Computer Applications, Volume 28–No.7, August 2011, pp. 26-35.
11. Li X. B., A scalable decision tree system and its application in pattern recognition and intrusion detection, Decision Support Systems, Vol. 41, Issue 1, November 2005, pp. 112-130.
12. MacQueen J., Some methods for classification and analysis of multivariate observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 1967, pp. 281-297.
13. Marinova V., A Short Survey of Intrusion Detection Systems. Problems of Engineering Cybernetics and Robotics, 58:23–30, 2007.
14. [UNM] University of New Mexico’s Computer Immune Systems Project, http://www.cs.unm.edu/~immsec/systemcalls.htm.
15. Wagner R. A., M. J. Fischer, The string-to-string correction problem, Journal of the Association for Computing Machinery 21, pp. 168-173, 1974.
1. Dagorn N., WebIDS: A Cooperative Bayesian Anomaly-Based Intrusion Detection System for Web Applications, Recent Advances in Intrusion Detection, LNCS, Vol. 5230/2008, Springer Berlin / Heidelberg, pp. 392-393.
2. Davies, D.L., Bouldin, D.W., (2000) A cluster separation measure, IEEE Trans. Pattern Anal. Machine Intell., 1(4), 1979, 224-227.
3. Dunn, J. (1974) Well separated clusters and optimal fuzzy partitions, Journal of Cybernetics , 4, 1974, 95-104.
4. Gorodetsky, V., Karsaeyv, O., Samoilov, V., Multi-agent technology for distributed data mining and classification, In Proceedings of IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003, pp. 438- 441.
5. Hubert L, Schultz J. Quadratic assignment as a general data-analysis strategy . British Journal of Mathematical and Statistical Psychologie, 1976; 190-241.
6. Ferri C., N. Lachinche, S. A. Macskassy, A. Rakotomamonjy, Second Workshop on ROC
Analysis in ML, 2005.
7. Forrest S., S.A. Hofmeyr, A. Somayaji, T.A. Longtaff, A Sense of Self for Unix Processes, In Proceedings of the 1996 IEEE Symposium on Security and Privacy, IEEE Computer Society Press, Los Alamitors, CA, pp.120-128.
8. Forrest S., S.A. Hofmeyr, A. Somayaji, Intrusion detection using sequences of system calls, Journal of Computer Security, Vol. 6, 1998, pp. 151-180.
9. Jaccard P., Distribution de la florine alpine dans la Bassin de Dranses et dans quelques regions voisines, Bulletin de la Societe Vaudoise des Sciences Naturelles, 37, 1901, 241-272.
10. Jyothsna V., V. V. Rama Prasad, K. Munivara Prasad, A Review of Anomaly based Intrusion Detection Systems, International Journal of Computer Applications, Volume 28–No.7, August 2011, pp. 26-35.
11. Li X. B., A scalable decision tree system and its application in pattern recognition and intrusion detection, Decision Support Systems, Vol. 41, Issue 1, November 2005, pp. 112-130.
12. MacQueen J., Some methods for classification and analysis of multivariate observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 1967, pp. 281-297.
13. Marinova V., A Short Survey of Intrusion Detection Systems. Problems of Engineering Cybernetics and Robotics, 58:23–30, 2007.
14. [UNM] University of New Mexico’s Computer Immune Systems Project, http://www.cs.unm.edu/~immsec/systemcalls.htm.
15. Wagner R. A., M. J. Fischer, The string-to-string correction problem, Journal of the Association for Computing Machinery 21, pp. 168-173, 1974.
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2018-05-29
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Компютърни науки и комуникации - рецензирани публикации. ISSN: 1314-7846
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ОЦЕНКА НА ЕФЕКТИВНОСТТА НА БАЗАТА НА АНОМАЛНИ ИДЕНТИФИКАЦИИ ОТНОСНО АЛГОРИТЪТ ЗА КЛЪСТЕРИИ И ДИНАМИКАТА НА ДАННИ ТЕХНИКИ. (2018). КОМПЮТЪРНИ НАУКИ И КОМУНИКАЦИИ, 2(3), 24-30. https://csc.bfu.bg/index.php/CSC/article/view/173
