Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2021

03.07.2020 | Original Article

Causative label flip attack detection with data complexity measures

verfasst von: Patrick P. K. Chan, Zhimin He, Xian Hu, Eric C. C. Tsang, Daniel S. Yeung, Wing W. Y. Ng

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A causative attack which manipulates training samples to mislead learning is a common attack scenario. Current countermeasures reduce the influence of the attack to a classifier with the loss of generalization ability. Therefore, the collected samples should be analyzed carefully. Most countermeasures of current causative attack focus on data sanitization and robust classifier design. To our best knowledge, there is no work to determinate whether a given dataset is contaminated by a causative attack. In this study, we formulate a causative attack detection as a 2-class classification problem in which a sample represents a dataset quantified by data complexity measures, which describe the geometrical characteristics of data. As geometrical natures of a dataset are changed by a causative attack, we believe data complexity measures provide useful information for causative attack detection. Furthermore, a two-step secure classification model is proposed to demonstrate how the proposed causative attack detection improves the robustness of learning. Either a robust or traditional learning method is used according to the existence of causative attack. Experimental results illustrate that data complexity measures separate untainted datasets from attacked ones clearly, and confirm the promising performance of the proposed methods in terms of accuracy and robustness. The results consistently suggest that data complexity measures provide the crucial information to detect causative attack, and are useful to increase the robustness of learning.
Vorheriger Artikel Accelerating ELM training over data streams
Nächster Artikel Clinical quantitative information recognition and entity-quantity association from Chinese electronic medical records

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Aha DW, Kibler D (1989) Noise-tolerant instance-based learning algorithms. In: Proceedings of the 11th international joint conference on artificial intelligence—Volume 1, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, IJCAI’89, pp 794–799
2.
Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Logic Soft Comput 17(2–3):255–287
3.
4.
Barreno M, Nelson B, Sears R, Joseph AD, Tygar JD (2006) Can machine learning be secure? In: Proceedings of the 2006 ACM symposium on information, computer and communications security, ACM, ASIACCS ’06, pp 16–25
5.
Barreno M, Nelson B, Joseph AD, Tygar JD (2010) The security of machine learning. Mach Learning 81(2):121–148MathSciNetCrossRef
6.
Bernado-Mansilla E, Ho TK (2005) Domain of competence of xcs classifier system in complexity measurement space. IEEE Trans Evolut Comput 9(1):82–104CrossRef
7.
Bhagoji AN, He W, Li B, Song D (2018) Practical black-box attacks on deep neural networks using efficient query mechanisms. In: European conference on computer vision, Springer, pp 158–174
8.
Biggio B (2010) Adversarial pattern classification. PhD thesis, University of Cagliari, Cagliari (Italy)
9.
Biggio B, Roli F (2018) Wild patterns: ten years after the rise of adversarial machine learning. Pattern Recognition 84:317–331CrossRef
10.
Biggio B, Fumera G, Roli F (2010) Multiple classifier systems for robust classifier design in adversarial environments. Int J Mach Learning Cybernet 1(1–4):27–41CrossRef
11.
Biggio B, Corona I, Fumera G, Giacinto G, Roli F (2011a) Bagging classifiers for fighting poisoning attacks in adversarial classification tasks. In: International workshop on multiple classifier systems. Springer, Berlin, pp 350–359
12.
Biggio B, Nelson B, Laskov P (2011b) Support vector machines under adversarial label noise. In: Journal of machine learning research—proc. 3rd Asian conference on machine learning (ACML 2011), Taoyuan, Taiwan, vol 20, pp 97–112
13.
Biggio B, Nelson B, Laskov P (2012) Poisoning attacks against support vector machines. In: 29th Int’l Conf. on Machine Learning (ICML), Omnipress
14.
Biggio B, Corona I, Maiorca D, Nelson B, Srndic N, Laskov P, Giacinto G, Roli F (2013) Evasion attacks against machine learning at test time. In: European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD), Springer-Verlag Berlin Heidelberg, vol 8190, pp 387–402
15.
Biggio B, Fumera G, Roli F (2014) Security evaluation of pattern classifiers under attack. IEEE Trans Knowl Data Eng 26:984–996CrossRef
16.
Biggio B, Corona I, He ZM, Chan PPK, Giacinto G, Yeung DS, Roli F (2015) One-and-a-half-class multiple classifier systems for secure learning against evasion attacks at test time. Int’l Workshop Multiple Classifier Syst (MCS) 9132:168–180CrossRef
17.
Britto AS, Sabourin R, Oliveira LE (2014) Dynamic selection of classifiersa comprehensive review. Pattern Recognition 47(11):3665–3680CrossRef
18.
Callison-Burch C, Dredze M (2010) Creating speech and language data with amazon’s mechanical turk. In: Proceedings of the NAACL HLT 2010 workshop on creating speech and language data with Amazon’s Mechanical Turk, Association for Computational Linguistics, pp 1–12
19.
Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: 2017 IEEE symposium on security and privacy (SP), IEEE, pp 39–57
20.
Chan PP, He ZM, Li H, Hsu CC (2018) Data sanitization against adversarial label contamination based on data complexity. Int J Mach Learn Cybernet 9(6):1039–1052CrossRef
21.
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surveys (CSUR) 41(3):15CrossRef
22.
Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3):27
23.
Cheng H, Yan X, Han J, Hsu CW (2007) Discriminative frequent pattern analysis for effective classification. In: IEEE 23rd international conference on data engineering, IEEE, pp 716–725
24.
Chung SP, Mok AK (2006) Allergy attack against automatic signature generation. In: Proceedings of the 9th international conference on recent advances in intrusion detection, Springer-Verlag, RAID’06, pp 61–80
25.
Cretu GF, Stavrou A, Locasto ME, Stolfo SJ, Keromytis AD (2008) Casting out demons: sanitizing training data for anomaly sensors. In: Security and privacy, 2008. SP 2008. IEEE symposium on, IEEE, pp 81–95
26.
Dalvi N, Domingos P, Mausam, Sanghai S, Verma D (2004) Adversarial classification. In: Proceedings of the Tenth ACM SIGKDD International conference on knowledge discovery and data mining, ACM, KDD ’04, pp 99–108
27.
Dekel O, Shamir O (2009) Good learners for evil teachers. In: Proceedings of the 26th annual international conference on machine learning, ACM, pp 233–240
28.
Demontis A, Biggio B, Fumera G, Giacinto G, Roli F (2017) Infinity-norm support vector machines against adversarial label contamination. In: ITASEC, pp 106–115
29.
30.
Fefilatyev S, Shreve M, Kramer K, Hall L, Goldgof D, Kasturi R, Daly K, Remsen A, Bunke H (2012) Label-noise reduction with support vector machines. In: 21st international conference on pattern recognition (ICPR), IEEE, pp 3504–3508
31.
Fierrez-Aguilar J, Ortega-Garcia J, Gonzalez-Rodriguez J, Bigun J (2005) Discriminative multimodal biometric authentication based on quality measures. Pattern Recognition 38(5):777–779CrossRef
32.
He ZM (2012) Cost-sensitive steganalysis with stochastic sensitvity and cost sensitive training error. Int Conf Mach Learn Cybernet 1:349–354
33.
He ZM, Chan PPK, Yeung DS, Pedrycz W, Ng WWY (2015) Quantification of side-channel information leaks based on data complexity measures for web browsing. Int J Mach Learn Cybernet 6(4):607–619CrossRef
34.
Ho TK (2002) A data complexity analysis of comparative advantages of decision forest constructors. Pattern Anal Appl 5(2):102–112MathSciNetCrossRef
35.
Ho TK, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24(3):289–300CrossRef
36.
Huang L, Joseph AD, Nelson B, Rubinstein BI, Tygar J (2011) Adversarial machine learning. In: Proceedings of the 4th ACM workshop on Security and artificial intelligence, ACM, pp 43–58
37.
Kantchelian A, Tygar J, Joseph A (2016) Evasion and hardening of tree ensemble classifiers. In: International conference on machine learning, pp 2387–2396
38.
Lakhina A, Crovella M, Diot C (2004) Diagnosing network-wide traffic anomalies. ACM SIGCOMM Comput Commun Rev ACM 34:219–230CrossRef
39.
Li B, Wang Y, Singh A, Vorobeychik Y (2016) Data poisoning attacks on factorization-based collaborative filtering. In: Advances in neural information processing systems, pp 1885–1893
40.
Lowd D, Meek C (2005) Adversarial learning. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, ACM, New York, NY, USA, KDD ’05, pp 641–647
41.
Luengo J, Herrera F (2012) Shared domains of competence of approximate learning models using measures of separability of classes. Inform Sci 185(1):43–65MathSciNetCrossRef
42.
Madani P, Vlajic N (2018) Robustness of deep autoencoder in intrusion detection under adversarial contamination. In: Proceedings of the 5th annual symposium and Bootcamp on hot topics in the science of security, ACM, p 1
43.
Mao K (2002) Rbf neural network center selection based on fisher ratio class separability measure. IEEE Trans Neural Netw 13(5):1211–1217CrossRef
44.
Nelson B (2010) Behavior of machine learning algorithms in adversarial environments. PhD thesis, EECS Department, University of California, Berkeley
45.
Nelson B, Barreno M, Chi FJ, Joseph AD, Rubinstein BI, Saini U, Sutton CA, Tygar JD, Xia K (2008) Exploiting machine learning to subvert your spam filter. LEET 8:1–9
46.
Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A (2016) The limitations of deep learning in adversarial settings. In: IEEE European symposium on security and privacy, IEEE, pp 372–387
47.
Papernot N, McDaniel P, Goodfellow I, Jha S, Celik ZB, Swami A (2017) Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia conference on computer and communications security, pp 506–519
48.
Pekalska E, Paclik P, Duin RPW (2002) A generalized kernel approach to dissimilarity-based classification. J Mach Learn Res 2:175–211MathSciNetMATH
49.
Ramachandran A, Feamster N, Vempala S (2007) Filtering spam with behavioral blacklisting. In: Proceedings of the 14th ACM conference on computer and communications security, ACM, pp 342–351
50.
Roli F, Biggio B, Fumera G (2013) Pattern recognition systems under attack. Progress in pattern recognition, image analysis, computer vision, and applications. Springer, Berlin, pp 1–8
51.
Rubinstein BI, Nelson B, Huang L, Joseph AD, Lau Sh, Rao S, Taft N, Tygar J (2009) Antidote: understanding and defending against poisoning of anomaly detectors. In: Proceedings of the 9th ACM SIGCOMM conference on internet measurement conference, ACM, pp 1–14
52.
SáEz JA, Luengo J, Herrera F (2013) Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognition 46(1):355–364CrossRef
53.
Sahami M, Dumais S, Heckerman D, Horvitz E (1998) A bayesian approach to filtering junk e-mail. In: Learning for text categorization: papers from the 1998 workshop, vol 62, pp 98–105
54.
Sánchez JS, Mollineda RA, Sotoca JM (2007) An analysis of how training data complexity affects the nearest neighbor classifiers. Pattern Anal Appl 10(3):189–201MathSciNetCrossRef
55.
Servedio RA (2003) Smooth boosting and learning with malicious noise. J Mach Learn Res 4:633–648MathSciNetMATH
56.
Smith FW (1968) Pattern classifier design by linear programming. IEEE Trans Comput 100(4):367–372CrossRef
57.
Smutz C, Stavrou A (2012) Malicious pdf detection using metadata and structural features. In: Proceedings of the 28th annual computer security applications conference, ACM, pp 239–248
58.
Soule A, Salamatian K, Taft N (2005) Combining filtering and statistical methods for anomaly detection. In: Proceedings of the 5th ACM SIGCOMM conference on internet measurement, USENIX Association
59.
Su J, Vargas DV, Sakurai K (2019) One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation
60.
Tuv E, Borisov A, Runger G, Torkkola K (2009) Feature selection with ensembles, artificial variables, and redundancy elimination. J Mach Learn Res 10(Jul):1341–1366MathSciNetMATH
61.
62.
Whitehill J, Wu Tf, Bergsma J, Movellan JR, Ruvolo PL (2009) Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. In: Advances in neural information processing systems, pp 2035–2043
63.
Xiao H, Xiao H, Eckert C (2012) Adversarial label flips attack on support vector machines. 20th European Conference on artificial intelligence (ECAI). Montepellier, France, pp 870–875
64.
Xiao H, Biggio B, Brown G, Fumera G, Eckert C, Roli F (2015a) Is feature selection secure against training data poisoning? In: Proceedings of The 32nd international conference on machine learning (ICML’15), pp 1689–1698
65.
Xiao H, Biggio B, Nelson B, Xiao H, Eckert C, Roli F (2015b) Support vector machines under adversarial label contamination. Neurocomputing 160:53–62CrossRef
66.
Zhang F, Chan PP, Tang TQ (2015) L-gem based robust learning against poisoning attack. In: 2015 International conference on wavelet analysis and pattern recognition (ICWAPR), IEEE, pp 175–178
67.
Zhang F, Chan P, Biggio B, Yeung D, Roli F (2016) Adversarial feature selection against evasion attacks. IEEE Trans Cybernet 46:766–777CrossRef
68.
Zügner D, Akbarnejad A, Günnemann S (2018) Adversarial attacks on neural networks for graph data. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, ACM, pp 2847–2856
Metadaten
Titel
Causative label flip attack detection with data complexity measures
verfasst von
Patrick P. K. Chan
Zhimin He
Xian Hu
Eric C. C. Tsang
Daniel S. Yeung
Wing W. Y. Ng
Publikationsdatum
03.07.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2021
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01159-7

Weitere Artikel der Ausgabe 1/2021

International Journal of Machine Learning and Cybernetics 1/2021 Zur Ausgabe

Original Article

Multi-objective unit commitment optimization with ultra-low emissions under stochastic and fuzzy uncertainties

Original Article

Efficient image segmentation through 2D histograms and an improved owl search algorithm

Original Article

Clinical quantitative information recognition and entity-quantity association from Chinese electronic medical records

Original Article

Attribute reduction in formal decision contexts and its application to finite topological spaces

Original Article

Discrete convolutional CRF networks for depth estimation from monocular infrared images

Original Article

Fine-grained pornographic image recognition with multiple feature fusion transfer learning

Neuer Inhalt

    Bildnachweise