Bayesian network and fuzzy logic

Can anyone give me an example of a Bayesian开发者_如何学运维 network and fuzzy logic being used in intrusion detection?

I'm struggling to figure out how it can be used. And any code on it?

Thanks guys.

The exact details will depend upon whether you're talking about a burglar alarm type situation (sensor readings) or something fancier involving security guards and sharks with lasers. Either way, the principle is the same.

You start with root nodes describing the basic things that affect intrusion, e.g.,

Sensor detected motion (true/false)  
Shark smelt blood (true/false)
Temperature (too low/just right/too high)
Security guard is asleep
...  
any other things you can think of.

You assign a probability to each state of each root node.

P(Security guard is asleep) = 0.25

Then you define child nodes that depend upon those root nodes, e.g., Security guard heard noise would depend upon Security guard is asleep.

You assign conditional probabilities for each state of the child nodes, given each state of its parents.

P(Security guard heard noise|Security guard is asleep) = 0.05
P(Security guard heard noise|Security guard is not asleep) = 0.5

Eventually, you'll want to get to an outcome like Burglary has been foiled.

Once you have your network node set up, you can evaluate it, and calculate the probability of different outcomes happening.

Next you add evidence. So if you know your shark smelt blood, that node gets set to a particular value and you can reevaluate the network to see how probabilities have changed.

In terms of software, the Bayes Net toolbox is well regarded.