A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection

Algorithm 1

The algorithm of RNSA.

𝑅𝑁𝑆𝐴(𝑇𝑟𝑎𝑖𝑛,𝑟_𝑑,𝑚𝑎𝑥𝑁𝑢𝑚,𝐷)

Input: the self training set 𝑇𝑟𝑎𝑖𝑛, the radius of detectors 𝑟_𝑑, the number of needed detectors 𝑚𝑎𝑥𝑁𝑢𝑚

Output: the detector set 𝐷

Step 1. Initialize the self training set 𝑇𝑟𝑎𝑖𝑛;

Step 2. Randomly generate a candidate detector 𝑑_new. Calculate the Euclidean distance between 𝑑_new and all the selves in 𝑇𝑟𝑎𝑖𝑛.

   If 𝑑𝑖𝑠(𝑑_new,𝑎𝑔)<𝑟_𝑑+𝑟_𝑠 for at least one self antigen 𝑎𝑔, execute Step 2; if not, execute Step 3.

Step 3. Add 𝑑_new into the detector set 𝐷;

Step 4. If the size of 𝐷 satisfies 𝑁_𝑑>𝑚𝑎𝑥𝑁𝑢𝑚, return 𝐷, and the process ends; if not, jump to Step 2.