Intrusion Detection in Wireless Sensor Networks with an Improved NSA Based on Space Division

Table 3

The detector generation process.

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SD-RNSA(Train, , )
Input: training set Train, expected coverage
Output: detector set
: sampling times in non-self-space,
 : number of non-self-samples
 : number of non-self-samples covered by detectors
CD: candidate detector set
SubSpaces: subspace set

 Step 1. Initialize the self-training set, , , , .
 Step 2. Invoke GenerateSpaces(Train, SubSpaces) to divide the space, and several subspaces SubSpaces are got.
 Step 3. A candidate detector is randomly generated, and find the subspace where is located.
 Step 4. The Euclidean distance between and both contained-selves and half-contained-selves in the subspace is calculated. If can be identified by a self , discard it and perform step 3. Otherwise, increase .
 Step 5. The Euclidean distance between and detectors in the subspace is calculated. If is not be identified by any detector , add it to the candidate detector set CD. Otherwise, increase and determine whether the algorithm reaches the expected coverage . If it is, return and the procedure ends.
 Step 6. Determine whether reaches the sampling times . if , add the detector in the candidate detector set CD to the collection , and reset , , and CD. Otherwise, perform step 3.

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