Page 1442 - (ISC)² CISSP Certified Information Systems Security Professional Official Study Guide

updating models of activity.

Machine learning techniques fall into two major categories.

Supervised learning techniques use labeled data for training. The
analyst creating a machine learning model provides a dataset along
with the correct answers and allows the algorithm to develop a
model that may then be applied to future cases. For example, if an
analyst would like to develop a model of malicious system logins,
the analyst would provide a dataset containing information about
logins to the system over a period of time and indicate which were
malicious. The algorithm would use this information to develop a

model of malicious logins.

Unsupervised learning techniques use unlabeled data for training.
The dataset provided to the algorithm does not contain the
“correct” answers; instead, the algorithm is asked to develop a
model independently. In the case of logins, the algorithm might be
asked to identify groups of similar logins. An analyst could then

look at the groups developed by the algorithm and attempt to
identify groups that may be malicious.

Neural Networks

In neural networks, chains of computational units are used in an
attempt to imitate the biological reasoning process of the human
mind. In an expert system, a series of rules is stored in a knowledge

base, whereas in a neural network, a long chain of computational
decisions that feed into each other and eventually sum to produce the
desired output is set up. Neural networks are an extension of machine
learning techniques and are also commonly referred to as deep
learning or cognitive systems.

Keep in mind that no neural network designed to date comes close to

having the reasoning power of the human mind. Nevertheless, neural
networks show great potential to advance the artificial intelligence
field beyond its current state. Benefits of neural networks include
linearity, input-output mapping, and adaptivity. These benefits are
evident in the implementations of neural networks for voice
recognition, face recognition, weather prediction, and the exploration