Page 1440 - (ISC)² CISSP Certified Information Systems Security Professional Official Study Guide

Understanding Knowledge-Based Systems

Since the advent of computing, engineers and scientists have worked
toward developing systems capable of performing routine actions that
would bore a human and consume a significant amount of time. The

majority of the achievements in this area have focused on relieving the
burden of computationally intensive tasks. However, researchers have
also made giant strides toward developing systems that have an
“artificial intelligence” that can simulate (to some extent) the purely
human power of reasoning.

The following sections examine two types of knowledge-based artificial
intelligence systems: expert systems and neural networks. We’ll also

take a look at their potential applications to computer security
problems.

Expert Systems

Expert systems seek to embody the accumulated knowledge of experts

on a particular subject and apply it in a consistent fashion to future
decisions. Several studies have shown that expert systems, when
properly developed and implemented, often make better decisions
than some of their human counterparts when faced with routine
decisions.

Every expert system has two main components: the knowledge base
and the inference engine.

The knowledge base contains the rules known by an expert system.
The knowledge base seeks to codify the knowledge of human experts
in a series of “if/then” statements. Let’s consider a simple expert
system designed to help homeowners decide whether they should
evacuate an area when a hurricane threatens. The knowledge base
might contain the following statements (these statements are for
example only):

If the hurricane is a Category 4 storm or higher, then flood waters
normally reach a height of 20 feet above sea level.