Papers 


  1.   Partial Computer Homeostasis Using Autonomous Epistemic Agents

       (Poster presented at KCAP 2015).

  1. Cameron Hughes, Ctest Laboratories

  2. Tracey Hughes, Ctest Laboratories

  3. Trevor Watkins, Kent State University

  4. James Dittrich, ASC (Advanced Software Construction)

justification_clusters_paper.html

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.