SBAD: Sequence based attack detection via sequence comparison

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6549 LNAI, Issue , 2011, Pages 78-91

  Mao, Ching Hao ^a   Pao, Hsing Kuo ^a   Faloutsos, Christos ^b   Lee, Hahn Ming ^a,c  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b Carnegie Mellon University ^*  (United States)

^c INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ATTACK DETECTION; BENCHMARK DATASETS; DATA SETS; DISSIMILARITY MEASURES; EXISTING SYSTEMS; GRAPH STRUCTURES; GRAPH-BASED; INTRUSION DETECTORS; LARGE-SCALE PROBLEM; NETWORK ATTACK; NETWORK MONITORING; PAYLOAD INFORMATION; PRIVACY INFORMATION; SECURITY OPERATION CENTER; SEQUENCE COMPARISONS;

COMPUTER CRIME; DATA MINING; DETECTORS; LEARNING SYSTEMS; NETWORK SECURITY;

DATA PRIVACY;

EID: 79955639952     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-19896-0_7     Document Type: Conference Paper

References (18)

1. Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. on Knowledge and Data Engineering 5(6), 914-925 (1993)
2. Ding, B., Lo, D., Han, J., Khoo, S.-C.: Efficient mining of closed repetitive gapped subsequences from a sequence database. In: ICDE 2009 (March 2009)
3. Exbrayat, M.: ECML/PKDD challenge: analyzing web traffic a boundaries signature approach. In: PKDD 2007, pp. 17-29 (2007)
4. Faloutsos, C., Lin, K.-I.D.: Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: ACM SIGMOD, May 23-25, pp. 163-174 (1995)
5. Ji, X., Bailey, J., Dong, G.: Mining minimal distinguishing subsequence patterns with gap constraints. In: ICDM 2005 (2005)
6. Ke, Y., Cheng, J., Yu, J.X.: Top-k correlative graph mining. In: SDM, pp. 1038-1049 (2009)
7. Keogh, E., Lonardi, S., Ratanamahatana, C.A.: Towards parameter-free data mining. In: KDD 2004, pp. 206-215 (2004) (Pubitemid 40114930)
8. Kohavi, R., Provost, F.: Glossary of terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process 30, 271-274 (1998)
9. Lane, T., Brodley, C.E.: An empirical study of two approaches to sequence learning for anomaly detection. Machine Learning 51(1), 73-107 (2004)
10. Law, M.H.C., Zhang, N., Jain, A.K.: Nonlinear manifold learning for data stream. In: Jonker, W., Petković, M. (eds.) SDM 2004. LNCS, vol. 3178. Springer, Heidelberg (2004)
11. Lee, Y.-J., Mangasarian, O.L.: SSVM: A smooth support vector machine for classification. Comput. Optim. Appl. 20(1), 5-22 (2001) (Pubitemid 32797935)
12. Li, M., Badger, J.H., Chen, X., Kwong, S., Kearney, P., Zhang, H.: An informationbased sequence distance and its application to whole mitochondrialgenome phylogeny. Bioinformatics 17(2), 149-154 (2001) (Pubitemid 32208523)
13. Li, M., Vitányi, P.: An Introduction to Kolmogorov Complexity and Its Applications, 2nd edn. Springer, New York (1997)
14. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: Fayyad, U.M., Uthurusamy, R. (eds.) KDD 1995 (1995)
15. Ning, P., Cui, Y., Reeves, D., Xu, D.: Techniques and tools for analyzing intrusion alerts. ACM Trans. Inf. Sys. Secur. 7(2), 274-318 (2004)
16. Pao, H.-K., Case, J.: Computing entropy for ortholog detection. In: International Conference on Computational Intelligence, pp. 89-92 (2004)
17. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319-2323 (2000) (Pubitemid 32041577)
18. Zhou, J., Heckman, M., Reynolds, B., Carlson, A., Bishop, M.: Modeling network intrusion detection alerts for correlation. ACM Trans. Inf. Sys. Secur. 10(1), 1-31 (2007)