System and method for assessing categorization rule selectivity
作者： Alexey E. Antonov , Alexey M. Romanenko
DOI:
关键词:
摘要: Assessment of selectivity categorization rules. One or more rules are applied to a set un-categorized objects produce result representing assignment the into at least two categories. A score for one rule is obtained based on statistical information. The numerical represents an estimation accuracy rule, and produced as application trained determination algorithm, which plurality specially-selected pre-categorized training data, with each producing uniform grouping objects.
lens.org 参考文章(24)
Mamoun Alazab, Sitalakshmi Venkatraman, Paul A Watters, Moutaz Alazab, None, Zero-day malware detection based on supervised learning algorithms of API call signatures australasian data mining conference. pp. 171- 182 ,(2011)
Ljupčo Todorovski, Sašo Džeroski, Combining Classifiers with Meta Decision Trees Machine Learning. ,vol. 50, pp. 223- 249 ,(2003) , 10.1023/A:1021709817809
Makoto Iwayama, System and program for generating boolean search formulas ,(2011)
Marc E. Seinfeld, Anil Francis Thomas, Jack Wilson Stokes, Ajith Kumar, Timothy Jon Fraser, Adrian M. Marinescu, Applying antimalware logic without revealing the antimalware logic to adversaries ,(2011)
Max J. Pucher, Method for training a system to specifically react on a specific input ,(2007)
Mark Kennedy, Systems and methods for using multiple in-line heuristics to reduce false positives ,(2009)
Johannes Fürnkranz, Peter A. Flach, An analysis of rule evaluation metrics international conference on machine learning. pp. 202- 209 ,(2003)
Erez Zadok, Eleazar Eskin, Manasi Bhattacharyya, Matthew G. Schultz, J Stolfo Salvatore, System and methods for detection of new malicious executables ,(2002)
Denis A. Nazarov, Silent-mode signature testing in anti-malware processing ,(2010)
Asaf Shabtai, Robert Moskovitch, Yuval Elovici, Chanan Glezer, Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey Information Security Technical Report. ,vol. 14, pp. 16- 29 ,(2009) , 10.1016/J.ISTR.2009.03.003