An application layer IDS security device uses a Bayes based learning machine alg

Question

An application layer IDS security device uses a Bayes based learning machine algorithm that is 90% sensitive (90% of the time identifies true positives) and 90% specific (90% of the time identifies true negatives). The IDS performs real time deep Malware Traffic Analysis in a high attack application environment that approximately 60% of the traffic is legitimate and 40% of the traffic is malware, in which case the device drops the malware packets. Calculate the probability of false negatives, which is the worst case scenario and is equivalent to the probability for a successful attack.

Note: Conditional Probability (A | B) = Probability (A and B) / Probability (B)

Explanation / Answer

Here we are given that:

P( legitimate ) = 0.6 and P( malware ) = 0.4

Also, we are given that:

P( positive | legitimate ) = 0.9 and P( negative | malware ) = 0.9

The probability of a false negative is computed as:

P( negative | legitimate ) = 1 - P( positive | legitimate ) = 1 - 0.9 = 0.1

Therefore 0.1 is the required probability of false negatives here.

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