Abstract: There is disclosed in one example a method of detecting computer malware, including: receiving a binary object for analysis; allocating the binary object to a sandbox; within the sandbox, loading the binary object into an executable memory region; performing a memory dump of the executable memory region; and analyzing the memory dump for malware characteristics.

Abstract: There is disclosed in one example a method of detecting computer malware, including: receiving a binary object for analysis; allocating the binary object to a sandbox; within the sandbox, loading the binary object into an executable memory region; performing a memory dump of the executable memory region; and analyzing the memory dump for malware characteristics.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve detection of malware in executable code. Examples disclosed herein include an apparatus comprising: a log file filtration controller to exclude at least one known clean function from a log file to generate a filtered log file; a log file normalization controller to normalize mnemonics of functions in the filtered log file to generate normalized functions; a feature vector generation controller to populate a feature vector with n-gram groupings of the normalized functions; and a machine learning engine to train a machine learning model with the feature vector, the machine learning model to be deployed to an end-user device to detect malware in executable code.