Multiple classifier systems for robust classifier design in adversarial environments

International Journal of Machine Learning and Cybernetics

Volumn 1, Issue 1-4, 2010, Pages 27-41

  Biggio, Battista ^a   Fumera, Giorgio ^a   Roli, Fabio ^a  

^a UNIVERSITY OF CAGLIARI   (Italy)

Author keywords

Adversarial classification; Linear classifiers; Multiple classifier systems; Robust classifiers

Indexed keywords

ADVERSARIAL CLASSIFICATION; ADVERSARIAL ENVIRONMENTS; BIOMETRIC AUTHENTICATION; CLASSIFICATION TASKS; CLASSIFIER DESIGN; CLASSIFIER ENSEMBLES; CURRENT THEORIES; DATA MANIPULATIONS; DESIGN METHOD; LINEAR CLASSIFIERS; MULTIPLE CLASSIFIER SYSTEMS; NETWORK INTRUSION DETECTION; RANDOM SUBSPACE METHOD; ROBUST CLASSIFIERS; SPAM FILTERING; VERIFICATION SYSTEMS;

BIOMETRICS; INTRUSION DETECTION;

PATTERN RECOGNITION SYSTEMS;

EID: 79952315022     PISSN: 18688071     EISSN: 1868808X     Source Type: Journal    
DOI: 10.1007/s13042-010-0007-7     Document Type: Article

References (53)

1. The Apache Spam Assassin Project. http://spamassassin.apache.org/.
2. Barreno M, Nelson B, Sears R, Joseph AD, Tygar JD (2006) Can machine learning be secure? In: ASIACCS '06: proceeding 2006 ACM symposium on information, computer and communications security, New York, NY, USA. ACM, New York, pp 16-25.
3. Benediktsson JA, Kittler J, Roli F (eds) (2009) Multiple classifier systems, 8th international workshop (MCS 2009). In: Lecture notes in computer science, vol 5519. Springer, New York.
4. Biggio B, Fumera G, Roli F (2008) Adversarial pattern classification using multiple classifiers and randomisation. In: 12th Joint IAPR international workshop on structural and syntactic pattern recognition (SSPR 2008). LNCS, vol 5342. Springer-Verlag, New York, pp 500-509.
5. Biggio B, Fumera G, Roli F (2009) Evade hard multiple classifier systems. In: Okun O, Valentini G (eds) Supervised and unsupervised ensemble methods and their applications. Studies in computational intelligence, vol 245. Springer, Berlin, pp 15-38.
6. Biggio B, Fumera G, Roli F (2009) Multiple classifier systems for adversarial classification tasks. In: Benediktsson JA, Kittler J, Roli F (eds) Multiple classifier systems, 8th international workshop (MCS 2009). Lecture notes in computer science, vol 5519. Springer, New York, pp 132-141.
7. Biggio B, Fumera G, Roli F (2010) Multiple classifier systems under attack. In: Gayar NE, Kittler J, Roli F (eds) MCS. Lecture notes in computer science. Springer, Berlin, pp 74-83.
8. Bishop CM (2007) Pattern recognition and machine learning (Information science and statistics), 1st edn. Springer, Berlin.
9. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123-140.
10. Breiman L (2001) Random forests. Mach Learn 45:5-32.
11. Bühlmann P, Yu B (2002) Analyzing bagging. Ann Stat 30(4): 927-961.
12. Buja A, Stuetzle W (2000) The effect of bagging on variance, bias, and mean squared error. Technical report. AT&T Labs-Research.
13. Cárdenas AA, Baras JS (2006) Evaluation of classifiers: practical considerations for security applications. In: AAAI workshop on evaluation methods for machine learning, Boston, MA, USA.
14. Chang C-C, Lin C-J (2001) LibSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
15. Cormack GV (2007) Trec 2007 spam track overview. In: Voorhees EM, Buckland LP (eds) TREC, volume special publication 500-274. National Institute of Standards and Technology (NIST).
16. Cretu GF, Stavrou A, Locasto ME, Stolfo SJ, Keromytis AD (2008) Casting out demons: sanitizing training data for anomaly sensors. In: IEEE symposium on security and privacy, pp 81-95.
17. Dalvi N, Domingos P, Mausam, Sanghai S, Verma D (2004) Adversarial classification. In: Tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD), Seattle, pp 99-108.
18. Domingos P (1997) Why does bagging work? a bayesian account and its implications. In: Proceedings of 3rd international conference on knowledge discovery and data mining, pp 155-158.
19. Drucker H, Wu D, Vapnik VN (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5):1048-1054.
20. Fogla P, Sharif M, Perdisci R, Kolesnikov O, Lee W (2006) Polymorphic blending attacks. In: USENIX-SS'06: proceedings of 15th conference on USENIX security symposium. USENIX Association.
21. Friedman JH, Hall P (2007) On bagging and nonlinear estimation. J Stat Plan Inference 137(3):669-683. Special issue on nonparametric statistics and related topics: in honor of M.L. Puri.
22. Galbally-Herrero J, Fierrez-Aguilar J, Rodriguez-Gonzalez JD, Alonso-Fernandez F, Ortega-Garcia J, Tapiador M (2006) On the vulnerability of fingerprint verification systems to fake fingerprint attacks. In: Proceedings of IEEE international Carnahan conference on security technology, ICCST, pp 130-136.
23. Gargiulo F, Kuncheva LI, Sansone C. Network protocol verification by a classifier selection ensemble. In: Benediktsson JA, Kittler J, Roli F (eds) (2009) Multiple classifier systems, 8th international workshop (MCS 2009). In: Lecture notes in computer science, vol 5519. Springer, New York, pp 314-323.
24. Globerson A, Roweis ST (2006) Nightmare at test time: robust learning by feature deletion. In: Cohen WW, Moore A (eds) ICML. ACM international conference proceeding series, vol 148. ACM, New York, pp 353-360.
25. Graham P (2002) A plan for spam. http://paulgraham.com/spam.html.
26. Graham-Cumming J (2004) How to beat an adaptive spam filter. In: MIT Spam conference, Cambridge, MA, USA.
27. Grandvalet Y (2004) Bagging equalizes influence. Mach Learn 55:251-270.
28. Haindl M, Kittler J, Roli F (eds) (2007) Multiple classifier systems. 7th international workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007. Proceedings, lecture notes in computer science, vol 4472. Springer, New York.
29. Hershkop S, Stolfo SJ (2005) Combining email models for false positive reduction. In: KDD '05: Proceedings of 11th ACM SIGKDD international conference on knowledge discovery in data mining. ACM, New York, pp 98-107.
30. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8): 832-844.
31. Jorgensen Z, Zhou Y, Inge M (2008) A multiple instance learning strategy for combating good word attacks on spam filters. J Mach Learn Res 9:1115-1146.
32. Kemmerer RA, Vigna G (2002) Intrusion detection: a brief history and overview (supplement to Computer magazine). Computer 35:27-30.
33. Kittler J, Hatef M, Duin RP, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226-239.
34. Kloft M, Laskov P. A 'poisoning' attack against online anomaly detection. In: Laskov P, Lippmann R (eds) Neural information processing systems (NIPS) workshop on machine learning in adversarial environments for computer security. http://mls-nips07.first.fraunhofer.de.
35. Kolcz A, Teo CH (2009) Feature weighting for improved classifier robustness. In: 6th conference on Email and Anti-Spam (CEAS).
36. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, Hoboken.
37. Laskov P, Kloft M (2009) A framework for quantitative security analysis of machine learning. In: AISec '09: proceedings of 2nd ACM workshop on security and artificial intelligence. ACM, New York, pp 1-4.
38. Laskov P, Lippmann R (eds) (2007) Neural information processing systems (NIPS) workshop on machine learning in adversarial environments for computer security. http://mls-nips07.first.fraunhofer.de.
39. Lewis DD (1992) An evaluation of phrasal and clustered representations on a text categorization task. In: SIGIR '92: proceedings of 15th annual international ACM SIGIR conference research and development in information retrieval, New York, NY, USA, pp 37-50.
40. Lowd D, Meek C (2005) Adversarial learning. In: Press A (ed) Proceedings of 11th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 641-647.
41. Lowd D, Meek C (2005) Good word attacks on statistical spam filters. In: 2nd conference on Email and Anti-Spam (CEAS).
42. Meyer TA, Whateley B (2004) Spambayes: effective opensource, bayesian based, email classification system. In: 1st conference on Email and Anti-Spam (CEAS).
43. Perdisci R, Dagon D, Lee W, Fogla P, Sharif M (2006) Misleading worm signature generators using deliberate noise injection. In: IEEE symposium on security and privacy, pp 15-31.
44. Perdisci R, Gu G, Lee W (2006) Using an ensemble of one-class svm classifiers to harden payload-based anomaly detection systems. In: International conference on data mining (ICDM). IEEE Computer Society, pp 488-498.
45. Rodrigues RN, Ling LL, Govindaraju V (2009) Robustness of multimodal biometric fusion methods against spoof attacks. J Vis Lang Comput 20(3):169-179.
46. Ross AA, Nandakumar K, Jain AK (2006) Handbook of multibiometrics. Springer, New York.
47. Skillicorn DB (2009) Adversarial knowledge discovery. IEEE Intell Syst 24:54-61.
48. Skurichina M, Duin RPW (1998) Bagging for linear classifiers. Pattern Recognit 31:909-930.
49. Skurichina M, Duin RPW (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5(2):121-135.
50. Stern H (2008) A survey of modern spam tools. In: 5th conference on Email and Anti-Spam (CEAS).
51. Sutton C, Sindelar M, McCallum A (2005) Feature bagging: preventing weight undertraining in structured discriminative learning. IR 402, University of Massachusetts.
52. Tran T, Tsai P, Jan T (2008) An adjustable combination of linear regression and modified probabilistic neural network for antispam filtering. In: International conference on pattern recognition (ICPR08), pp 1-4.
53. Uludag U, Jain AK (2004) Attacks on biometric systems: a case study in fingerprints. In: Proceedings of SPIE-EI 2004, security, steganography and watermarking of multimedia contents VI, pp 622-633.