ABSTRACT
The proliferation of mobile computing, the Internet of Things, hosting services, and cloud computing has increased the burden of computer log file analysis for system administrators, network analysts, security analysts, and large server hosting organizations. This is due to the voluminous amounts of log entries now produced by these technologies. Since log file analysis is used to monitor and control the overall health of the computer systems behind these technologies, it has become increasingly important. The spike in the
number of log entries has made real-time log analysis by human effort untenable and automated real-time log analysis essential. The log analysis process often requires human insight and judgment before a diagnosis or information synthesis becomes apparent. So while automated log analysis methods are essential, they must also be knowledge-based to be effective. In this paper, we describe a knowledge-based approach to partial computer self-regulation that uses autonomous epistemic agents to analyze and diagnose syslog entries in real-time, using a priori and posteriori knowledge of log file analysis within a hybrid deductive abductive first order logic model. The epistemic agent uses its a priori knowledge of Unix/Linux-based computer systems in conjunction with posteriori knowledge extracted from log file entries to uncover negative and positive scenarios and take advantage of opportunities to regulate a computer system's homeostasis.
