Abstract: The disclosed configurations generate synthetic attack data that emulates the markers of a cyberattack. The generated synthetic attack data is used to train an attack detection machine learning model that detects and mitigates actual cyberattacks in real-time. Synthetic attack data is generated by a synthetic attack data generation model, which is trained with a synthetic attack data generation prompt. The synthetic attack data generation prompt is constructed out of attack data samples and a prompt guideline. The prompt guideline is created from attack procedure descriptions, such as security blog posts or other write-ups about actual cyberattacks. Prompt guidelines may include samples of actual attack data that indicate how to format synthetic attack data. Once deployed, the attack detection machine learning model infers the occurrence of a cyberattack from log entries. Detected cyberattacks may be mitigated in an automated or semi-automated manner.

Abstract: Disclosed herein is a system and method for determining whether two files are similar or an unknown file contains malware or other malicious activity. The system takes a suspect file and generates a hash for the file. The hash represents segments of a file that may be compared with segments of other hashes. This hash is then compared with the hash of another file. The comparison measures the distance between the two hashes and if the two hashes are close enough to each other then the two files are consider similar to each other.