Bayes theorem Question

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Suppose that every packet observed by a network-based intrusion detection system (NIDS) belongs to one of the following mutually exclusive categories:

  • legitimate ($88\%$)
  • known worm ($4\%$)
  • distributed denial of service ($4\%$)
  • port scan ($4\%$)

The NIDS correctly classifies all known-worm packets.

A legitimate packet is classified as legitimate with probability $91\%$, and misclassified as belonging to any of the three attack categories with equal probability.

A DDoS packet is classified as DDoS with probability $50\%$, as a known worm with probability $40\%$, and as a legitimate packet with probability $10\%$.

A port-scan packet is classified correctly with probability $85\%$, and misclassified as a legitimate packet with probability $15\%$.

If the NIDS announces that a particular packet belongs to a known worm, what is the probability that this packet is not a legitimate packet?

Let the following:

  • $L$ denote the event that a given packet is legitimate
  • $W$ denote the event that it is a worm
  • $D$ denote the event that it is DDoS
  • $S$ denote the event that it is a port scan
  • $A$ denote the event that the worm alarm is raised

What I tried to is calculate $P(D\mid A)+P(W\mid A)+P(S\mid A)$.

Is there something wrong with my approach?