Literatur über die Missbrauchserkennung

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Tom Fawcett hat eine Bibliographie erstellt.
Missbrauchserkennung in mobilen Kommunikationsnetzwerken im ASPeCT Projekt.
Einige Artikel der Statistik-Gruppe bei Lucent.
Eine Bibliographie über die Missbrauchserkennung bei der Universität Karlsruhe.
Computer Fraud & Security

1997 AAAI Workshop "AI Approaches to Fraud Detection and Risk Management"
1998 AAAI Workshop "The Methodology of Applying Machine Learning"
1998 AI Fall Symposium on Artificial Intelligence and Link Analysis
International Conference on Fighting Mobile FraudLondon, 1997
Research Priorities in Wireless and Mobile Communications and Networking. Report of a Workshop held in March 1997, sponsored by the National Science Foundation, Division of Networking and Communications Research and Infrastructure.

[AFR97] Emin Aleskerov, Bernd Freisleben, Bharat Rao. CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection. In: Proceedings of Computa- tional Intelligence for Financial Engineering (CIFEr), S. 220--226, 1997.
[AME98] Dean W. Abbott, I. Philip Matkovsky und John F. Elder. An Evaluation of High-End Data Mining Tools for Fraud Detection. In: Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 2836-2841, 1998.
[ATW97] Suhaya Abu-Hakima, Mansour Toloo, Tony White. A Multi-Agent Systems Approach for Fraud Detection in Personal Communication Systems. In: [Faw97], 1997.
[Axe99] Stefan Axelsson. The Base-Rate Fallacy and its Implications for the Difficulty of Intrusion Detection. In: Proceedings of the 6th ACM Conference on Computer and Communications Security, pp. 1-7, 1999.
[BH] Richard J. Bolton, David J. Hand Statistical Fraud Detection: A Review. Statistical Science, 17(3), 235-255.
[BLH99a] R. Brause, T. Langsdorf, M. Hepp. Credit Card Fraud Detection by Adaptive Neural Data Mining. Internal Report 7/99, FB Informatik, University of Frankfurt a.M., 1999
[BLH99b] R. Brause, T. Langsdorf, M. Hepp. Neural Data Mining for Credit Card Fraud Detection. In: Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence. pp. 103--106. 1999.
[BS97a] Peter Burge, John Shawe-Taylor. Detecting Cellular Fraud Using Adaptive Prototypes. In: [Faw97].
[BS97b] Peter Burge, John Shawe-Taylor. Fraud-Management Tools: First Prototype. ASPeCT -- Project, Januar 1997. See [ASPeCT].
[BSCMPS97] P. Burge, J. Shawe-Taylor, C. Cooke, Y. Moreau, B. Preneel, C. Stoermann. Fraud Detection and Management in Mobile Telecommunications Networks.
[CCLPS00] Michael Cahill, Fei Chen, Diane Lambert, José Pinheiro, Don X. Sun. Detecting Fraud in the Real World. In: Handbook of Massive Datasets. Kluewer. 2002.
[CFPS99] Philip K. Chan, Wei Fan, Andreas L. Prodromidis, Salvatore J. Stolfo. Distributed Data Mining in Credit Card Fraud Detection. In: IEEE Intelligent Systems, Bd. 14, Nr. 6, S. 67--74, 1999.
[CLPS99] Fei Chen, Diane Lambert, José Pinheiro, Don Sun. Reducing Transaction Databases, Without Lagging Behind the Data or Losing Information. Unpublished, 1999.
[DB98] Steven K. Donoho, Scott W. Bennett. Fraud Detection and Discovery.
[DC98] J. R. Dorronsoro, C. Santa Cruz. Discrimination of overlapping data and credit card fraud detection. Technischer Bericht, Department of Computer Engineering, Universidad de Madrid, 1998.
[DGSC97] Jose R. Dorronsoro, Francisco Ginel, Carmen Sanchez, Carlos Santa Cruz. Neural Fraud Detection in Credit Card Operations. In: IEEE Transactions on Neural Networks, Nr. 4, Bd. 8, Juli 1997.
[EN96] Kazuo J. Ezawa, Steven W. Norton. Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts. IEEE Expert, Nr. 5, Bd. 11, S. 45--51, 1996.
[Faw97] Tom Fawcett. AI Approaches to Fraud Detection & Risk Management --- Papers from the 1997 AAAI Workshop, Technical Report WS-97-07, Juli 1997, AAAI-Press.
[FP97a] Tom Fawcett and Foster Provost. Adaptive Fraud Detection. Data Mining and Knowledge Discovery, vol. 1, no. 3, p. {291-316}. 1997.
[FP97b] Tom Fawcett, Foster Provost. Combining Data Mining and Machine Learning for Effective Fraud Detection. In: [Faw97].
[Gos97] Phil Gosset. Fraud Detection Concepts: Final Report. ASPeCT -- Project, November 1997. See [ASPeCT].
[GH99] Phil Gossett, Mark Hyland. Classification, Detection and Prosecution of Fraud on Mobile Networks. Proceedings of ACTS Mobile Summit, Sorrento, Italy, Juni 1999.
[GR94] Sushmito Ghosh, Douglas L. Reilly. Credit Card Fraud Detection with a Neural-Network. In: Proceedings of the 27th Hawaii International Conference on Information Systems, S. 621-- 630, 1994.
[HDA98] Mark Hyland, Jos Dumortier, Diana Alonso Blas. Legal Aspects of Fraud Detection. ASPeCT-Project. See [ASPeCT].
[HS08] Constantinos S. Hilas, Paris As. Mastorocostas. An Application of Supervised and Unsupervised Learning Approaches to Telecommunications Fraud Detection. Knowledge-Based Systems, 21, pp 721 – 726, 2008. doi:10.1016/j.knosys.2008.03.026.
[HS09] Constantinos S. Hilas, Paris As. Mastorocostas. Designing an expert system for fraud detection in a private telecommunications network. An Application of Supervised and Unsupervised Learning Approaches to Telecommunications Fraud Detection. Expert Systems with Applications. 2009. doi: 10.1016/j.eswa.2009.03.031.
[HS05] Constantinos S. Hilas, John N. Sahalos. User profiling for fraud detection in telecommunication networks. In: 5th International Conference on Technology and Automation, Thessaloniki, Greece, October 2005. pp 382-387.
[HS06] Constantinos S. Hilas, John N. Sahalos. Testing the fraud detection ability of different user profiles by means of FFNN classifiers. In: Collias St. et al ed.. Lecture Notes in Computer Science, vol. 4132, Part II, 2006. pp 872-883.
[HS07] Constantinos S. Hilas, John N. Sahalos. An application of decision trees for rule extraction towards telecommunications fraud detection. In: B. Apolloni et al. (Eds.): KES 2007/ WIRN 2007, Lecture Notes in Artificial Intelligence, vol. 4693, Part II, Springer. 2007, pp. 1112–1121.
[Jen97] David Jensen. Prospective Assessment of AI Technologies for Fraud Detection: A Case Study.
[KKN99] Daniel A. Keim, Eleftherios E. Koutsofios, Stephen C. North. Visual Exploration of Large Telecommunication Data Sets. In: User Interfaces to Data Intensive Systems, S. 12-- 20, 1999.
[MP96] Yves Moreau, Bart Preneel. Definition of Fraud Detection Concepts. ASPeCT -- Project, August 1996. See [ASPeCT].
[OTA95] U. S. Congress, Office of Technology Assessment. Information Technologies for Control of Money Laundering. U. S. Government Printing Office, OTA-ITC-630, Washington DC, September 1995.
[PF97] Foster Provost, Tom Fawcett. Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997.
[PF01] Foster Provost, Tom Fawcett. Robust Classification for Imprecise Environments. In: Machine Learning, vol. 42, no. 3, pp. 203-231, 2001.
[PFK98] Foster Provost, Tom Fawcett, Ron Kohavi. The Case Against Accuracy Estimation for Comparing Induction Algorithms. Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), July 1998.
[Sal97] Steven Salzberg. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. In: Data Mining and Knowledge Discovery, Nr. 3, S. 317--328, 1997.
[SFLPC97] Salvatore J. Stolfo, David W. Fan, Wenke Lee, Andreas L. Prodromidis, Philip K. Chan. Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results. In: [Faw97].
[Stö97] Christof Störmann. Fraud Management Tool: Evaluation Report. ASPeCT - Project, Oktober 1997. See [ASPeCT].

Literatur über die Missbrauchserkennung

Artikel

Workshops und Konferenzen

Bibliographie