| | Input: Packet set P, K, f1, f2, m, c, n, epoches | | | Output: detection result R | | (1) | pool = multiprocessing.Pool(processes = m) | | (2) | NCSS, R = [] | | (3) | While i in range(0, n) do //The network of first n period is normal. | | (4) | SS, NCSSi = [] | | (5) | Bucket_List = pool.map_async(ipsketch, Pi).get() | | (6) | obtain NCSSi with lines 05–09 of algorithm 1 | | (7) | NCSS.add(NCSSi) | | (8) | End while | | (9) | Traindataset = NCSS.reshape(–1, K/c, K/c,1) | | (10) | AOCC.train(Traindataset, epoches) | | (11) | obtain the optimal parameters P_best | | (12) | While t is continue do | | (13) | SS, NCSSt = [] | | (14) | Bucket_List = pool.map_async(ipsketch, Pi).get() | | (15) | obtain NCSSt with lines (4)–(8) of algorithm 1 | | (16) | Testt = CSSt.reshape(–1, K/c, K/c,1) | | (17) | Rt = AOCC.test(Testt, P_best) | | (18) | R.add(Rt) | | (19) | End While |
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