Abstract
A keylogger is a technology that secretly monitors user input on a system. Attackers use keyloggers as malicious software to steal personal information and credentials, in particular logins and passwords on banking websites and electronic payment systems. The availability and heterogeneity of keyloggers makes the task of detecting them one of the information security priorities. This paper presents an approach to keylogger detection based on a complex of artificial intelligence methods. These methods allow one to monitor the behavior of malware in the system and identify implicit patterns to detect potential danger, without the presence of specific signatures.
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