OC.L10N.register(
    "suspicious_login",
    {
    "New login location detected" : "偵測到新的賬號登入位置",
    "More info about the suspicious IP address available on %s" : "關於可疑 IP 地址的更多資訊，請見 %s",
    "A new login into your account was detected. The IP address %s was classified as suspicious. If this was you, you can ignore this message. Otherwise you should change your password." : "偵測到您賬號有新的帳戶登入活動。此IP位址%s歸類為可疑。如果是您本人的登入活動，可以忽略此訊息。否則您應更改密碼。",
    "Open %s ↗" : "開啟 %s ↗",
    "Suspicious Login" : "可疑的賬號登入活動",
    "New login detected" : "偵測到新的賬號登入活動",
    "Detect and warn about suspicious IPs logging into Nextcloud\n\t" : "對關於IP來源登入Nextcloud活動進行偵測與警告",
    "More information ↗" : "更多資訊 ↗",
    "You can get more info by pressing the button which will open %s and show info about the suspicious IP-address." : "您可以通過按下按鈕來獲取更多信息，該按鈕將打開 %s 並顯示有關可疑 IP 地址的信息。",
    "Suspicious login detection" : "偵測到可疑的賬號登入活動",
    "The suspicious login app is enabled on this instance. It will keep track of IP addresses users successfully log in from and build a classifier that warns if a new login comes from a suspicious IP address." : "此為偵測可疑賬號登入活動的實用App。它將追蹤成功登入帳戶的IP位址，並構建分類器，如果新賬號登入來自可疑IP位址，則該分類器將發出警示。",
    "Training data statistics" : "訓練資料統計分析",
    "So far the app has captured {total} logins (including client connections), of which {distinct} are distinct (IP, UID) tuples." : "目前此應用程式已取用 {total} 個登入紀錄（包括客戶端的連線），其中 {distinct} 個是不同的（IP，UID）元素組。",
    "IPv4" : "IPv4",
    "IPv6" : "IPv6",
    "Classifier model statistics" : "分類器模型統計分析",
    "No classifier model has been trained yet. This most likely means that you just enabled the app recently. Because the training of a model requires good data, the app waits until logins of at least {days} days have been captured." : "尚未訓練分類器模型。這很可能因為您剛剛啟用此應用程序。由於訓練模型需要品質良好的數據資料，因此該應用程式將等到獲取到至少{days}天的登入信息為止。",
    "During evaluation, the latest model (trained {time}) has shown to capture {recall}% of all suspicious logins (recall), whereas {precision}% of the logins classified as suspicious are indeed suspicious (precision). Below you see a visualization of historic model performance." : "在評估模型過程中，已顯示最新模型（經過{time}的訓練）已攔截了所有可疑登入（或提取過往的）的 {recall}％，而被分類為可疑登入的{precision}％是確實可疑的（精確度）。您可以瀏覽下方顯示的視覺化歷史模型表現。",
    "Precision" : "準確度",
    "Recall" : "提取紀錄"
},
"nplurals=1; plural=0;");
