A machine-implemented method for assessing the selectivity of categorization rules used for malware detection of un-categorized files, the method comprising: autonomously applying at least one categorization rule to a set of un-categorized files, for example a files database, to assign each of the un-categorized files into one category of at least two categories; autonomously gathering statistical information relating to use of the at least one categorization rule on the set of files belonging to each of said at least two categories based on properties relating to operation of the at least one categorization rule to assign the set of un-categorized files to one of the at least two categories, wherein the statistical information of use of the at least one categorization rule includes at least one of: an elapsed time since a prior application of the at least one categorization rule, a number of files categorized into each category by the at least one categorization rule, average minimum and maximum sizes of each file assigned by the at least one categorization rule to each category, a number of unique compilers used to create each categorized file, a number of unique packagers in each categorized file, or any combination thereof; autonomously adapting at least one trained data analysis algorithm by accessing malicious and safe training files from a training files database and obtaining the statistical information of a plurality of categorization rules when categorizing said training files, such that a machine learning technique adapts the at least one trained data analysis algorithm based on the obtained statistical information; based on the at least one trained data analysis algorithm, calculating a numerical selectivity score for the at least one categorization rule, the numerical selectivity score representing an estimation of accuracy of the at least one categorization rule to assign un-categorized files to one category of the at least two categories, wherein the selectivity score is calculated using the gathered statistical information relating to use of the at least one categorization rule on the set of files belonging to each of said at least two categories based on properties relating to operation of the at least one categorization rule to assign the set of un-categorized files to one of the at least two categories; and determining, based on the numerical selectivity score, a use of the at least one categorization rule for the malware detection. autonomously applying at least one categorization rule to a set of un-categorized files, for example a files database, to assign each of the un-categorized files into one category of at least two categories; autonomously gathering statistical information relating to use of the at least one categorization rule on the set of files belonging to each of said at least two categories based on properties relating to operation of the at least one categorization rule to assign the set of un-categorized files to one of the at least two categories, wherein the statistical information of use of the at least one categorization rule includes at least one of: an elapsed time since a prior application of the at least one categorization rule, a number of files categorized into each category by the at least one categorization rule, average minimum and maximum sizes of each file assigned by the at least one categorization rule to each category, a number of unique compilers used to create each categorized file, a number of unique packagers in each categorized file, or any combination thereof; autonomously adapting at least one trained data analysis algorithm by accessing malicious and safe training files from a training files database and obtaining the statistical information of a plurality of categorization rules when categorizing said training files, such that a machine learning technique adapts the at least one trained data analysis algorithm based on the obtained statistical information; based on the at least one trained data analysis algorithm, calculating a numerical selectivity score for the at least one categorization rule, the numerical selectivity score representing an estimation of accuracy of the at least one categorization rule to assign un-categorized files to one category of the at least two categories, wherein the selectivity score is calculated using the gathered statistical information relating to use of the at least one categorization rule on the set of files belonging to each of said at least two categories based on properties relating to operation of the at least one categorization rule to assign the set of un-categorized files to one of the at least two categories; and determining, based on the numerical selectivity score, a use of the at least one categorization rule for the malware detection.