Question

Software filters rely heavily on “blacklists” (lists of known “phishing” URLs) to detect fraudulent e-mails. But such filters typically catch only 33 percent of phishing URLs. Jason receives 23 phishing e-mails.

Answer 1

Answer on Question #58716 – Math – Statistics and Probability

Question

Software filters rely heavily on “blacklists” (lists of known “phishing” URLs) to detect fraudulent e-mails. But such filters typically catch only 33 percent of phishing URLs. Jason receives 23 phishing e-mails.

(a)

What is the expected number that would be caught by such a filter? (Round your answer to the next whole number.)

(b)

What is the chance that such a filter would detect none of them? (Round your answer to 5 decimal places.)

Solution

(a) Expected number is $E = 23 * 0.33 = 7.59 \approx 8$.

(b) Probability that such a filter would detect none of them is

$$P(x = 0) = (1 - 0.33)^{23} = 0.00010.$$

Answer: (a) $7.59 \approx 8$; (b) 0.00010.

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