Abstract: In accordance with one embodiment of the present disclosure, a method for determining the similarity between a first data set and a second data set is provided. The method includes performing an entropy analysis on the first and second data sets to produce a first entropy result, wherein the first data set comprises data representative of a first one or more computer files of known content and the second data set comprises data representative of a one or more computer files of unknown content; analyzing the first entropy result; and if the first entropy result is within a predetermined threshold, identifying the second data set as substantially related to the first data set.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: In accordance with one embodiment of the present disclosure, a method for determining the similarity between a first data set and a second data set is provided. The method includes performing an entropy analysis on the first and second data sets to produce a first entropy result, wherein the first data set comprises data representative of a first one or more computer files of known content and the second data set comprises data representative of a one or more computer files of unknown content; analyzing the first entropy result; and if the first entropy result is within a predetermined threshold, identifying the second data set as substantially related to the first data set.

Abstract: In accordance with one embodiment of the present disclosure, a method for determining the similarity between a first data set and a second data set is provided. The method includes performing an entropy analysis on the first and second data sets to produce a first entropy result, wherein the first data set comprises data representative of a first one or more computer files of known content and the second data set comprises data representative of a one or more computer files of unknown content; analyzing the first entropy result; and if the first entropy result is within a predetermined threshold, identifying the second data set as substantially related to the first data set.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: In accordance with one embodiment of the present disclosure, a method for determining the similarity between a first data set and a second data set is provided. The method includes performing an entropy analysis on the first and second data sets to produce a first entropy result, wherein the first data set comprises data representative of a first one or more computer files of known content and the second data set comprises data representative of a one or more computer files of unknown content; analyzing the first entropy result; and if the first entropy result is within a predetermined threshold, identifying the second data set as substantially related to the first data set.

Abstract: In accordance with one embodiment of the present disclosure, a method for determining the similarity between a first data set and a second data set is provided. The method includes performing an entropy analysis on the first and second data sets to produce a first entropy result, wherein the first data set comprises data representative of a first one or more computer files of known content and the second data set comprises data representative of a one or more computer files of unknown content; analyzing the first entropy result; and if the first entropy result is within a predetermined threshold, identifying the second data set as substantially related to the first data set.

Abstract: In accordance with one embodiment of the present disclosure, a method for determining the similarity between a first data set and a second data set is provided. The method includes performing an entropy analysis on the first and second data sets to produce a first entropy result, wherein the first data set comprises data representative of a first one or more computer files of known content and the second data set comprises data representative of a one or more computer files of unknown content; analyzing the first entropy result; and if the first entropy result is within a predetermined threshold, identifying the second data set as substantially related to the first data set.