Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: Some embodiments of an efficient string search have been presented. In one embodiment, a string of bytes representing content written in a non-delimited language is received, wherein the content has been classified into a predetermined category. In a single pass through the string of bytes, a set of N-grams is searched for simultaneously. Statistical information on occurrences of the N-grams, if any, in the string of bytes is collected. In some embodiments, a model is generated based on the statistical information, where the model is usable by a content filter to classify content.

Abstract: Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.

Abstract: A training procedure for N-gram based statistical document classification has been disclosed. In one embodiment, a set of N-grams is selected out of a second set of N-grams, each of the N-grams having a sequence of N bytes, where N is an integer. Then a statistical content classification model is generated based on occurrences of the N-grams, if any, in a set of training documents and a set of validation documents. The statistical content classification model is provided to content filters to classify content.