Spyware prevention by classifying end user license agreements

Studies in Computational Intelligence

Volumn 134, Issue , 2008, Pages 373-382

  Lavesson, Niklas ^a   Davidsson, Paul ^a   Boldt, Martin ^a   Jacobsson, Andreas ^a  

^a BLEKINGE INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

EULA spyware classification prevention

Indexed keywords

EID: 45949092426     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-79355-7_36     Document Type: Article

References (18)

1. Boldt, M., Jacobsson, A., Lavesson, N., Davidsson, P.: Automated Spyware Detection Using End User License Agreements. In: 2nd International Conference on Information Security and Assurance, IEEE Press, New York (2008)
2. Adaware, http://www.lavasoft.com
3. EULA Analyzer, http://www.spywareguide.com/analyze
4. EULAlyzer, http://www.javacoolsoftware.com/eulalyzerpro.html
5. Cohen, W.: Learning Rules that Classify E-Mail. In: Advances in Inductive Logic Programming, IOS Press, Amsterdam (1996)
6. Drucker, H., Wu, D., Vapnik, V.: Support Vector Machines for Spam Categorization. IEEE Transactions on Neural Networks 10(5), 1048-1054 (1999)
7. Koprinska, I., Poon, J., Clark, J., Chan, J.: Learning to Classify E-Mail. Information Sciences 177, 2167-2187 (2007)
8. CNET Download.com, http://www.download.com
9. Spyware Guide, http://www.spywareguide.com
10. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
11. Frank, E., Bouckaert, R.R.: Naive Bayes for Text Classification with Unbalanced Classes. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 503-510. Springer, Heidelberg (2006)
12. Provost, F., Fawcett, T., Kohavi, R.: The Case against Accuracy Estimation for Comparing Induction Algorithms. In: 15th International Conference on Machine Learning, pp. 445-453. Morgan Kaufmann, San Francisco (1998)
13. Provost, F., Fawcett, T.: Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In: 3rd International Conference on Knowledge Discovery and Data Mining, pp. 43-48. AAAI Press, Menlo Park (1997)
14. Nadeau, C., Bengio, Y.: Inference for the Generalization Error. Machine Learning 52(3), 239-281 (2003)
15. McCallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI 1998 Workshop on Learning for Text Categorization, pp. 41-48. AAAI Press, Menlo Park (1998)
16. Kibriya, A.M., Frank, E., Pfahringer, B., Holmes, G.: Multinomial Naive Bayes for Text Categorization Revisited. In: 7th Australian Joint Conference on Artificial Intelligence, pp. 488-499. Springer, Berlin (2004)
17. Lavesson, N., Davidsson, P.: Quantifying the Impact of Learning Algorithm Parameter Tuning. In: 21st National Conference on Artificial Intelligence, pp. 395-400. AAAI Press, Menlo Park (2006)
18. Lavesson, N., Davidsson, P.: Generic Methods for Multi-criteria Evaluation. In: SIAM International Conference on Data Mining (2008)