Research Article
Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection
| Input: training sample set U, sample instances to be classified , and category label | | Output: sample J belongs to category | | z = number of categories | | Obtain the prior probability U (cx) | | for each x in z | | t = 0 | | s = 0 | | for each x in U | | t = t + 1 | | if (I ∈ cx) s = s + 1 | | end for | | U (cx) = s/t | | | | for each x in z | | U (J | cx) = 1 | | for each y in | | weight (x, y) = WJS (x, y) WICF () | | U (J | cx) = U (J | cx) U ( | cx) weight (x, y) | | end for | | ux = U(cx) U (J | cx) | | end for | | | | end for | | output () |
|