-
3
-
-
33845989821
-
Can machine learning be secure?
-
New York, NY, USA, ACM
-
M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar. Can machine learning be secure? In Proceedings of the 2006 ACM Symposium on Information, computer and communications security, pages 16-25, New York, NY, USA, 2006. ACM.
-
(2006)
Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security
, pp. 16-25
-
-
Barreno, M.1
Nelson, B.2
Sears, R.3
Joseph, A.D.4
Tygar, J.D.5
-
6
-
-
12244295760
-
Adversarial classification
-
New York, NY, USA, ACM
-
N. Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma. Adversarial classification. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '04, pages 99-108, New York, NY, USA, 2004. ACM.
-
(2004)
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '04
, pp. 99-108
-
-
Dalvi, N.1
Domingos, P.2
Mausam3
Sanghai, S.4
Verma, D.5
-
8
-
-
78049529865
-
Learning to classify with missing and corrupted features
-
O. Dekel, O. Shamir, and L. Xiao. Learning to classify with missing and corrupted features. Machine Learning, 81(2):149-178, 2010.
-
(2010)
Machine Learning
, vol.81
, Issue.2
, pp. 149-178
-
-
Dekel, O.1
Shamir, O.2
Xiao, L.3
-
9
-
-
29744452906
-
-
Technical Report UCB/CSD-03-1279, EECS Department, University of California, Berkeley, Oct
-
L. El Ghaoui, G. R. G. Lanckriet, and G. Natsoulis. Robust classification with interval data. Technical Report UCB/CSD-03-1279, EECS Department, University of California, Berkeley, Oct 2003.
-
(2003)
Robust Classification with Interval Data
-
-
El Ghaoui, L.1
Lanckriet, G.R.G.2
Natsoulis, G.3
-
10
-
-
34547359214
-
Evading network anomaly detection systems: Formal reasoning and practical techniques
-
New York, NY, USA, ACM
-
P. Fogla and W. Lee. Evading network anomaly detection systems: formal reasoning and practical techniques. In Proceedings of the 13th ACM conference on Computer and communications security, CCS '06, pages 59-68, New York, NY, USA, 2006. ACM.
-
(2006)
Proceedings of the 13th ACM Conference on Computer and Communications Security, CCS '06
, pp. 59-68
-
-
Fogla, P.1
Lee, W.2
-
13
-
-
76749092270
-
The weka data mining software: An update
-
November
-
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. SIGKDD Explor. Newsl., 11:10-18, November 2009.
-
(2009)
SIGKDD Explor. Newsl.
, vol.11
, pp. 10-18
-
-
Hall, M.1
Frank, E.2
Holmes, G.3
Pfahringer, B.4
Reutemann, P.5
Witten, I.H.6
-
14
-
-
78651359266
-
Classifier evaluation and attribute selection against active adversaries
-
January
-
M. Kantarcioglu, B. Xi, and C. Clifton. Classifier evaluation and attribute selection against active adversaries. Data Min. Knowl. Discov., 22:291-335, January 2011.
-
(2011)
Data Min. Knowl. Discov.
, vol.22
, pp. 291-335
-
-
Kantarcioglu, M.1
Xi, B.2
Clifton, C.3
-
15
-
-
0027640858
-
Learning in the presence of malicious errors
-
M. Kearns and M. Li. Learning in the presence of malicious errors. SIAM Journal on Computing, 22:807-837, 1993.
-
(1993)
SIAM Journal on Computing
, vol.22
, pp. 807-837
-
-
Kearns, M.1
Li, M.2
-
16
-
-
0042967740
-
A robust minimax approach to classification
-
G. R. G. Lanckriet, L. E. Ghaoui, C. Bhattacharyya, and J. M. I. A robust minimax approach to classification. Journal of Machine Learning Research, 3:555-582, 2002.
-
(2002)
Journal of Machine Learning Research
, vol.3
, pp. 555-582
-
-
Lanckriet, G.R.G.1
Ghaoui, L.E.2
Bhattacharyya, C.3
I, J.M.4
-
17
-
-
33751064824
-
Hamsa: Fast signature generation for zero-day polymorphic worms with provable attack resilience
-
IEEE Computer Society
-
Z. Li, M. Sanghi, Y. Chen, M.-Y. Kao, and B. Chavez. Hamsa: Fast signature generation for zero-day polymorphic worms with provable attack resilience. In Proceedings of the 2006 IEEE Symposium on Security and Privacy. IEEE Computer Society, 2006.
-
(2006)
Proceedings of the 2006 IEEE Symposium on Security and Privacy
-
-
Li, Z.1
Sanghi, M.2
Chen, Y.3
Kao, M.-Y.4
Chavez, B.5
-
18
-
-
77955660961
-
Mining adversarial patterns via regularized loss minimization
-
October
-
W. Liu and S. Chawla. Mining adversarial patterns via regularized loss minimization. Mach. Learn., 81:69-83, October 2010.
-
(2010)
Mach. Learn.
, vol.81
, pp. 69-83
-
-
Liu, W.1
Chawla, S.2
-
21
-
-
27544498978
-
Polygraph: Automatically generating signatures for polymorphic worms
-
IEEE Computer Society
-
J. Newsome, B. Karp, and D. X. Song. Polygraph: Automatically generating signatures for polymorphic worms. In 2005 IEEE Symposium on Security and Privacy, 8-11 May 2005, Oakland, CA, USA, pages 226-241. IEEE Computer Society, 2005.
-
(2005)
2005 IEEE Symposium on Security and Privacy, 8-11 May 2005, Oakland, CA, USA
, pp. 226-241
-
-
Newsome, J.1
Karp, B.2
Song, D.X.3
-
22
-
-
33751051489
-
Misleadingworm signature generators using deliberate noise injection
-
R. Perdisci, D. Dagon, W. Lee, P. Fogla, and M. Sharif. Misleadingworm signature generators using deliberate noise injection. In Proceedings of the 2006 IEEE Symposium on Security and Privacy, pages 17-31, 2006.
-
(2006)
Proceedings of the 2006 IEEE Symposium on Security and Privacy
, pp. 17-31
-
-
Perdisci, R.1
Dagon, D.2
Lee, W.3
Fogla, P.4
Sharif, M.5
-
24
-
-
33750335757
-
Anagram: A content anomaly detector resistant to mimicry attack
-
K. Wang, J. J. Parekh, and S. J. Stolfo. Anagram: A content anomaly detector resistant to mimicry attack. In Recent Advances in Intrusion Detection, 9th International Symposium, pages 226-248, 2006.
-
(2006)
Recent Advances in Intrusion Detection, 9th International Symposium
, pp. 226-248
-
-
Wang, K.1
Parekh, J.J.2
Stolfo, S.J.3
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