Using attack-specific feature subsets for network intrusion detection

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

Volumn 4304 LNAI, Issue , 2006, Pages 305-311

  Shin, Sung Woo ^a   Lee, Chi Hoon ^b  

^a School of Business Administration ^*  (South Korea)

^b Sungkyunkwan University   (South Korea)

Author keywords

Class specific learning; Data mining; Feature selection; Genetic algorithm; Network intrusion detection

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; GENETIC ALGORITHMS; INTRUSION DETECTION; LEARNING ALGORITHMS; SET THEORY;

CLASS SPECIFIC FEATURES; FALSE POSITIVE AND FALSE NEGATIVES; NETWORK INTRUSION DETECTION; NETWORK INTRUSION DETECTION SYSTEMS; REAL-TIME ENVIRONMENT; REDUNDANT FEATURES; SALIENT FEATURES; SPECIFIC LEARNING;

FEATURE EXTRACTION;

EID: 84886409746     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11941439_34     Document Type: Conference Paper

References (14)

1. Chebrolu, S., A. Abraham, J. Thomas, 2005, Feature deduction and ensemble design of intrusion detection systems, Computers &Security, 24, 295-307
2. Fayyad, U. M., K. B. Irani, 1993, Multi-interval discretization of continuous-valued attributes for classification learning, Proc. of 13th International Joint Conf. on Artificial Intelligence, 1022-1027.
3. Giacinto, G., F. Roli, L. Didaci, 2003, Fusion of multiple classifiers for intrusion detection in computer networks, Pattern Recognition Letters, 24, 1795-1803.
4. Han, S. J., S-B., Cho, 2003, Detecting intrusion with rule-based integration of multiple models, Computers &Security, 22, 613-623.
5. Hansman, S. Hunt, R., 2005, A Taxonomy of network and computer attacks, Computers &Security, 24, 31-43.
6. Kim, S. H., Shin, S. W., 2000, Identifying the impact of decision variables for nonlinear classification, Expert Systems With Applications, 18, 201-214.
7. Langley, P. and Sage. S., 1994, Induction of Selective Bayesian classifiers, Proc. of the 10th Conference on Uncertainty in Artificial Intelligence, 399-406.
8. Mahoney, M. V., P. K. Chan, 2003, An analysis of the 1999 DARPA/Lincoln Laboratory evaluation data for network anomaly detection. Proc. 6th Intl. Symposium on Recent Advances in Intrusion Detection (RAID 2003) 53, LNCS 2820, 220-237
9. McHugh, J. 2000. Testing intrusion detection systems: A critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory. ACM Trans. Information System Security 3 (4), 262-294.
10. Mukkamala, S., Andrew H. Sunga, A. Abraham, 2005, Intrusion detection using an ensemble of intelligent paradigms, Journal of Network and Computer Applications, 28, 167-182.
11. Punch, W. F., Goodman, E. D., et al., 1993, Further research on feature selection and classification using genetic algorithms, Int. Conf. on Genetic Algorithms, 557-564.
12. Stolfo, S., W. Fan, W. Lee, A. Prodromidis, P. K. Chan. 2000, Cost-based Modeling for Fraud and Intrusion Dectection: Results from the JAM Project, DARPA Information Survivability Conference.
13. Stolfo, S., W. Lee, P. K. Chan, W. Fan, and E. Eskin. 2001, Data mining-based intrusion detectors: An overview of the Columbia IDS project, ACM SIGMOD Record, 30 (4), 5-14.
14. Zhang, C., J. Jiang, M. Kamel, 2005, A Intrusion detection using hierarchical neural networks, Pattern Recognition Letters, 779-791.