-
2
-
-
0242625275
-
Predicting rare classes: can boosting make any weak learner strong?
-
ACM, New York, NY, USA
-
Joshi M.V., Agarwal R.C., and Kumar V. Predicting rare classes: can boosting make any weak learner strong?. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2002) 297-306
-
(2002)
Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, pp. 297-306
-
-
Joshi, M.V.1
Agarwal, R.C.2
Kumar, V.3
-
3
-
-
27144549260
-
Editorial: special issue on learning from imbalanced data sets
-
Chawla N.V., Japkowicz N., and Kotcz A. Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6 1 (2004) 1-6
-
(2004)
SIGKDD Explorations
, vol.6
, Issue.1
, pp. 1-6
-
-
Chawla, N.V.1
Japkowicz, N.2
Kotcz, A.3
-
5
-
-
0038011179
-
Specification-based anomaly detection: a new approach for detecting network intrusions
-
ACM Press
-
Sekar R., Gupta A., Frullo J., Shanbhag T., Tiwari A., Yang H., and Zhou S. Specification-based anomaly detection: a new approach for detecting network intrusions. Proceedings of the 9th ACM Conference on Computer and Communications Security (2002), ACM Press 265-274
-
(2002)
Proceedings of the 9th ACM Conference on Computer and Communications Security
, pp. 265-274
-
-
Sekar, R.1
Gupta, A.2
Frullo, J.3
Shanbhag, T.4
Tiwari, A.5
Yang, H.6
Zhou, S.7
-
6
-
-
0034455983
-
Adaptive intrusion detection: a data mining approach
-
Lee W., Stolfo S.J., and Mok K.W. Adaptive intrusion detection: a data mining approach. Artificial Intelligence Review 14 6 (2000) 533-567
-
(2000)
Artificial Intelligence Review
, vol.14
, Issue.6
, pp. 533-567
-
-
Lee, W.1
Stolfo, S.J.2
Mok, K.W.3
-
7
-
-
33745163595
-
Learning intrusion detection: Supervised or unsupervised?
-
Proc. ICIAP, September
-
P. Laskov, P. Düssel, C. Schäfer, K. Rieck, Learning intrusion detection: supervised or unsupervised? Proc. ICIAP 2005, September. Lecture Notes in Computer Science, LNCS 3617 (2005) 50-57.
-
(2005)
Lecture Notes in Computer Science, LNCS
, vol.3617
, pp. 50-57
-
-
Laskov, P.1
Düssel, P.2
Schäfer, C.3
Rieck, K.4
-
10
-
-
26944437843
-
Adaptive network intrusion detection method based on PCA and support vector machines, ADMA 2005, Lecture Notes in Artificial Intelligence
-
X. Xu, X.N. Wang, Adaptive network intrusion detection method based on PCA and support vector machines, ADMA 2005, Lecture Notes in Artificial Intelligence, LNAI 3584 (2005) 696-703.
-
(2005)
LNAI
, vol.3584
, pp. 696-703
-
-
Xu, X.1
Wang, X.N.2
-
12
-
-
0037209446
-
Host-based intrusion detection using dynamic and static behavioral models
-
Yeung D.Y., and Ding Y.X. Host-based intrusion detection using dynamic and static behavioral models. Pattern Recognition 36 (2003) 229-243
-
(2003)
Pattern Recognition
, vol.36
, pp. 229-243
-
-
Yeung, D.Y.1
Ding, Y.X.2
-
13
-
-
1942436335
-
Robustness of the Markov-Chain model for cyber-attack detection
-
Ye N., Zhang Y., and Borror C.M. Robustness of the Markov-Chain model for cyber-attack detection. IEEE Transactions on Reliability 53 1 (2004) 116-123
-
(2004)
IEEE Transactions on Reliability
, vol.53
, Issue.1
, pp. 116-123
-
-
Ye, N.1
Zhang, Y.2
Borror, C.M.3
-
16
-
-
6344255762
-
Next generation intrusion detection: autonomous reinforcement learning of network attacks
-
Cannady J. Next generation intrusion detection: autonomous reinforcement learning of network attacks. 23th National Information Systems Security Conference (2000)
-
(2000)
23th National Information Systems Security Conference
-
-
Cannady, J.1
-
17
-
-
77649275039
-
-
http://www.cs.unm.edu/∼immsec/data-sets.htm.
-
-
-
-
19
-
-
33847202724
-
Learning to predict by the method of temporal differences
-
Sutton R. Learning to predict by the method of temporal differences. Machine Learning 3 1 (1988) 9-44
-
(1988)
Machine Learning
, vol.3
, Issue.1
, pp. 9-44
-
-
Sutton, R.1
-
20
-
-
0032313923
-
Intrusion detection using sequences of systems call
-
Hofmeyr S., et al. Intrusion detection using sequences of systems call. Journal of Computer Security 6 (1998) 151-180
-
(1998)
Journal of Computer Security
, vol.6
, pp. 151-180
-
-
Hofmeyr, S.1
-
22
-
-
0031143730
-
An analysis of temporal difference learning with function approximation
-
Tsitsiklis J.N., and Roy B.V. An analysis of temporal difference learning with function approximation. IEEE Transactions on Automatic Control 42 5 (1997) 674-690
-
(1997)
IEEE Transactions on Automatic Control
, vol.42
, Issue.5
, pp. 674-690
-
-
Tsitsiklis, J.N.1
Roy, B.V.2
-
23
-
-
0036832950
-
Technical update: least-squares temporal difference learning
-
Boyan J.A. Technical update: least-squares temporal difference learning. Machine Learning 49 (2002) 233-246
-
(2002)
Machine Learning
, vol.49
, pp. 233-246
-
-
Boyan, J.A.1
-
25
-
-
24944517527
-
Learning classifiers for misuse detection using a bag of system calls representation
-
P. Kantor et al, Eds, ISI
-
D.K. Kang, D. Fuller, V. Honavar, Learning classifiers for misuse detection using a bag of system calls representation, in: P. Kantor et al. (Eds.), ISI 2005, Lecture Notes in Computer Science, 3495 (2005) 511-516.
-
(2005)
Lecture Notes in Computer Science
, vol.3495
, pp. 511-516
-
-
Kang, D.K.1
Fuller, D.2
Honavar, V.3
-
28
-
-
58549089680
-
Mohammad Rahmati, and Abdolreza Mirzaei, intrusion detection using fuzzy association rules
-
Tajbakhsh A. Mohammad Rahmati, and Abdolreza Mirzaei, intrusion detection using fuzzy association rules. Applied Soft Computing 9 2 (2009) 462-469
-
(2009)
Applied Soft Computing
, vol.9
, Issue.2
, pp. 462-469
-
-
Tajbakhsh, A.1
-
29
-
-
32344452166
-
-
Proceedings of the Third SIAM International Conference on Data Mining
-
Lazarevic A., Ertoz L., Kumar V., Ozgur A., and Srivastava J. A comparative study of anomaly detection schemes in network intrusion detection. Proceedings of the Third SIAM International Conference on Data Mining (2003) 25-36
-
(2003)
A comparative study of anomaly detection schemes in network intrusion detection
, pp. 25-36
-
-
Lazarevic, A.1
Ertoz, L.2
Kumar, V.3
Ozgur, A.4
Srivastava, J.5
|