一個簡單的端口掃描程序：
addstr = "發現端口" & Str(port) & "打開" & Space(5) Select Case port Case 7 plus = "ECHO服務" Case 21 plus = "FTP服務★★★★★" Case 25 plus = "SMTP服務★★" Case 43 plus = "Whois服務" Case 53 plus = "DNS服務☆" Case 80 plus = "HTTP服務★★★★★" Case 81 plus = "HOSTS2名稱服務" Case 99 plus = "NCX99后門★★★★★" Case 110 plus = "POP3服務★★★★★" Case 111 plus = "Sun RPC服務★★" Case 135 plus = "本地服務" Case 137 plus = "NETBIOS 名稱服務/流影POP" Case 138 plus = "NETBIOS 數據存儲服務" Case 139 plus = "NETBIOS 會議服務★★★" Case 119 plus = "NNTP虛擬服務" Case 163 plus = "SNMP服務" Case 443 plus = "https服務" Case 512 plus = "遠程執行服務" Case 513 plus = "login, remote login" Case 514 plus = "cmd, exec with auto auth." Case 600 plus = "Sun IPC服務器" Case 1080 plus = "SOCKS代理服務★★★" Case 1433 plus = "SQL服務器★★★" Case 3128 plus = "HTTP代理服務" Case 7626 plus = "木馬冰河★★★★★" Case 8080 plus = "HTTP代理服務" Case Else plus = "未知服務!"
The last step in training phase is refinement of the clusters
found above. Although DynamicClustering counters all the
basic k-means disadvantages, setting the intra-cluster similarity
r may require experimentation. Also, a cluster may
have a lot in common with another, i.e., sequences assigned
to it are as close to it as they are to another cluster. There
may also be denser sub-clusters within the larger ones.
