6 Theories of Probability and 6 Reasons Why They Matter to ISRA

While probably everyone would agree that information security risk analysis (ISRA) is shot through with appeals to probability, very few non-academic discussions of ISRA provide any sort of rigorous analysis of what “probability” means. (See Alberts and Dorofee 2003 for a notable exception.) In this blog post, I will try to fill that gap and provide a brief overview of the basic interpretations of probability. I will then explain why these distinctions matter to ISRA.

6 Theories of Probability

It may come as a surprise to some that there actually a variety of interpretations or theories of probability. All of the theories about probability can be divided into two groups: objective and non-objective.

Objective Theories of Probability

Objective theories of probability define probability values in a way that makes them independent of opinion. For example, consider what it means to talk about the probability of a compromise of a web server. According to objective theories of probability, there is only correct probability value (or range of values), period, and it doesn’t matter what you or I or anyone else thinks. There are many different types of objective probability, including the frequency theory, the logical theory, and the propensity theory.

The classical theory of probability is the oldest and probably best-known theory of probability. It treats all possible outcomes as equally probable. Thus, given a fair, standard six-sided die, the probability that any one side will land is 1/6.

The aleatory or frequency theory of probability is arguably the best-known theory of probability; it is also an empirical theory that views probability as a feature of the natural world. Anyone who has taken a mathematics course on statistics or probability theory has learned frequency probability. The frequency theory of probability defines probability as the frequency with which an outcome appears in a long series of similar events (Gillies 2000, p. 1).  Note that what the mathematician calls “frequency,” the information security professional has traditionally called the “rate of occurrence.” (For example, the auto insurance industry computes the “rate of occurrence” of theft for your vehicle, based on historical data about thefts in your location, the number of thefts involving your type of car, your claim history, and so forth.) In order to apply the frequency interpretation of probability, one must have sufficient data in order to arrive at a statistically valid conclusion about the frequency of the event in question.


  1. Russell Thomas Sep 8, 2010 at 10:23 am | Permalink

    Great post, Jeff.

    One thing I’ll add is to counter the criticism that ISRA relies on *predictions* of the future, which is another way of saying “knowledge about the future”. Most InfoSec people, in their gut, feel that such knowledge is unattainable or infeasable.

    But ISRA is really not about predicting the future or having highly certain knowledge about the future. Instead, it’s benefit is to help us ORGANIZE OUR UNCERTAINTY. It’s the systematic treatment of uncertainty and ignorance in all it’s forms, with a goal of promoting continuous learning and adaptation.

    Russell Cameron Thomas

  2. Jeff Lowder Sep 8, 2010 at 2:10 pm | Permalink

    Thanks, Russell. I’m glad you liked the post!

    Regarding the issue of ‘predicting’ the future, I think I agree with your point, but I would word it in a slightly different way. I would say that risk analyses do make ‘predictions’ about the future, but these predictions are hedged in various ways. For example, personal probabilities and intersubjective probabilities represent our degrees of belief (and, accordingly, our uncertainty) regarding various information security-related hazards. Additionally, as my discussion of single-case probabilities hopefully makes clear, frequency probabilities typically don’t make a prediction about a single event. On the other hand, estimated relative frequencies do … estimate the actual relative frequency in the real world, and hence the corresponding ‘actual’ frequency probability. Thus, for example, an ISRA may not provide an inductively correct argument for concluding that this web server will be attacked at this time, but it may be able to show that some system will be attacked at some time during a given time span. In that sense, I would say that ISRA does make predictions. This does not deny what I think is your point, however, that the criticism of ISRA falsely assumes that ISRA is committed to making a series of predictions about single events.


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