Add files via upload

Driver/Protect/Install_Driver.bat

@@ -1,34 +0,0 @@
-@echo off
-setlocal
-
-:: Check for permissions
->nul 2>&1 "%SYSTEMROOT%\system32\icacls.exe" "%SYSTEMROOT%\system32\config\system"
-
-if %errorlevel% NEQ 0 goto NoAdmin
-goto gotAdmin
-
-:NoAdmin
-    echo You do not have administrator rights, please run this script as administrator.
-    pause
-    exit /B
-
-:gotAdmin
-    if exist "%temp%\getadmin.vbs" del "%temp%\getadmin.vbs"
-
-set "script_dir=%~dp0"
-set "script_dir=%script_dir:~0,-1%"
-
-bcdedit -debug on
-bcdedit /set testsigning on
-bcdedit /deletevalue {current} safeboot
-
-powershell.exe -ExecutionPolicy Bypass -Command ^
-"$endDate = (Get-Date).AddYears(100); ^
- $cert = New-SelfSignedCertificate -DnsName PYAS -CertStoreLocation cert:\LocalMachine\My -Type CodeSigning -NotAfter $endDate; ^
- $pwd = ConvertTo-SecureString -String 'PYAS' -Force -AsPlainText; ^
- Export-PfxCertificate -Cert $cert -FilePath '%script_dir%\PYAS.pfx' -Password $pwd;"
-"%script_dir%\signtool.exe" sign /f "%script_dir%\PYAS.pfx" /p PYAS "%script_dir%\PYAS_Driver.sys"
-sc create PYAS_Driver type= kernel start= demand binPath= "%script_dir%\PYAS_Driver.sys"
-
-shutdown -r -t 0
-endlocal

Driver/Protect/Uninstall_Driver.bat

@@ -1,25 +0,0 @@
-@echo off
-setlocal
-
-:: Check for permissions
->nul 2>&1 "%SYSTEMROOT%\system32\icacls.exe" "%SYSTEMROOT%\system32\config\system"
-
-if %errorlevel% NEQ 0 goto NoAdmin
-goto gotAdmin
-
-:NoAdmin
-    echo You do not have administrator rights, please run this script as administrator.
-    pause
-    exit /B
-
-:gotAdmin
-    if exist "%temp%\getadmin.vbs" del "%temp%\getadmin.vbs"
-
-sc stop PYAS_Driver
-sc delete PYAS_Driver
-
-bcdedit /set testsigning off
-bcdedit -debug off
-
-shutdown -r -t 0
-endlocal

Engine/Model/PYAS_Model.json

@@ -1,11 +0,0 @@
-{
-    "Suffix": [".com", ".exe", ".dll", ".sys", ".scr", ".bat", ".cmd", ".ps1", ".vbs", ".wsf"], 
-
-    "Labels": ["Pefile/White", "Script/White", "Pefile/General", "Script/General"], 
-
-    "Detect": ["Pefile/General", "Script/General"],  
-
-    "Resize": [224, 224, 1, "nearest"], 
-
-    "Values": 95
-}

Engine/Model/convert.py

@@ -0,0 +1,73 @@
+from PIL import Image
+import numpy, pefile, os
+
+def file_to_images(file_data, output_dir, file_name, target_size=(224, 224)):#
+    wah = int(numpy.ceil(numpy.sqrt(len(file_data))))
+    file_data = numpy.frombuffer(file_data, dtype=numpy.uint8)
+    image_array = numpy.zeros((wah * wah,), dtype=numpy.uint8)
+    image_array[:len(file_data)] = file_data
+    image = Image.fromarray(image_array.reshape((wah, wah)), 'L')
+    image = image.resize(target_size, Image.Resampling.NEAREST)
+    image.save(os.path.join(output_dir, f"{file_name}.png"))
+
+def check_file_type(file_path):
+    try:
+        shell_section = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", 
+        "cry", "tvm", "dec", "enc", "vmp", "upx", "aes", "lzma", "press", 
+        "pack", "enigma", "protect", "secur"]
+
+        unknown_section = ["asmstub", "base", "bss", "clr_uef", "cursors", 
+        "engine", "fio", "fothk", "h~;", "icapsec", "malloc_h", "miniex", 
+        "mssmixer", "ndr64", "nsys_wr", "obr", "wow", "wow64svc", "wpp_sf",
+        "pad", "pgae", "poolmi", "proxy", "qihoo", "res", "retpol", "uedbg",
+        "rwexec", "rygs", "s:@", "sanontcp", "segm", "test", "tracesup",
+        "transit", "trs_age", "wisevec"]
+
+        unimportant_section = ["viahwaes", "orpc", "nep", "ace", "extjmp", 
+        "no_bbt", "data", "page", "hexpthk"]
+
+        shells = shell_section + unimportant_section + unknown_section
+        
+        ftype = str(f".{file_path.split('.')[-1]}").lower()
+
+        if ftype in [".bat", ".cmd", ".ps1", ".vbs", ".wsf", ".js", ".html"]:
+            with open(file_path, 'rb') as f:
+                return [f.read()]
+
+        match_data = []
+        if ftype in [".com", ".exe", ".dll", ".sys", ".scr"]:
+            with pefile.PE(file_path, fast_load=True) as pe:
+                for section in pe.sections:
+                    section_name = section.Name.rstrip(b'\x00').decode('latin1')
+                    #if (section.Characteristics & 0x00000020 and not
+                    #any(shell in section_name.lower() for shell in shells)):
+                    if section.Characteristics & 0x00000020:
+                        match_data.append(section.get_data())
+                return match_data
+        return False
+    except Exception as e:
+        print(e)
+        return False
+
+def batch_file_to_images(input_dir, output_dir):
+    for root, dirs, files in os.walk(input_dir):
+        for file in files:
+            try:
+                file_path = os.path.join(root, file)
+                file_data = check_file_type(file_path)
+                if file_data:
+                    for i, data in enumerate(file_data, 1):
+                        print(i, file_path)
+                        relative_path = os.path.relpath(root, input_dir)
+                        output_path_dir = os.path.join(output_dir, relative_path)
+                        if not os.path.exists(output_path_dir):
+                            os.makedirs(output_path_dir)
+                        file_to_images(data, output_path_dir, f"{i}_{file}")
+            except Exception as e:
+                print(e)
+                pass
+
+input_directory = input('Enter the folder path: ')
+output_directory = "./Image_File"
+batch_file_to_images(input_directory, output_directory)
+input('Conversion Complete')

Engine/Model/train.py

@@ -0,0 +1,111 @@
+import tensorflow as tf
+import os, tf2onnx, numpy, random
+from PIL import Image
+from tensorflow.keras import *
+
+Image.MAX_IMAGE_PIXELS = None
+tf.keras.mixed_precision.set_global_policy('mixed_float16')
+
+def get_file_list(directory):
+    file_paths, labels, class_indices = [], [], {}
+    for i, cls in enumerate(sorted([d for d in os.listdir(directory) if os.path.isdir(os.path.join(directory, d))])):
+        class_indices[cls] = i
+        cls_dir = os.path.join(directory, cls)
+        for root, _, files in os.walk(cls_dir):
+            for file in files:
+                if file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.gif')):
+                    file_paths.append(os.path.join(root, file))
+                    labels.append(i)
+    return file_paths, labels, class_indices
+
+def load_dataset(train_dir, val_dir, val_split=0.01, batch_size=128, color_mode="grayscale"):
+    channels = 1 if color_mode == "grayscale" else 3
+    if train_dir == val_dir:
+        file_paths, labels, class_indices = get_file_list(train_dir)
+        combined = list(zip(file_paths, labels))
+        random.shuffle(combined)
+        file_paths[:], labels[:] = zip(*combined)
+        num_val = int(val_split * len(file_paths))
+        train_files = file_paths[num_val:]
+        train_labels = labels[num_val:]
+        val_files = file_paths[:num_val]
+        val_labels = labels[:num_val]
+    else:
+        train_files, train_labels, class_indices = get_file_list(train_dir)
+        val_files, val_labels, _ = get_file_list(val_dir)
+    num_classes = len(class_indices)
+
+    def parse_fn(filename, label):
+        img = tf.io.read_file(filename)
+        img = tf.image.decode_image(img, channels=channels, expand_animations=False)
+        img = tf.cast(img, tf.float32) / 255.0
+        label = tf.one_hot(label, depth=num_classes)
+        return img, label
+
+    train_ds = tf.data.Dataset.from_tensor_slices((train_files, train_labels))
+    train_ds = train_ds.map(parse_fn, num_parallel_calls=tf.data.AUTOTUNE)
+    train_ds = train_ds.batch(batch_size).prefetch(tf.data.AUTOTUNE)
+    val_ds = tf.data.Dataset.from_tensor_slices((val_files, val_labels))
+    val_ds = val_ds.map(parse_fn, num_parallel_calls=tf.data.AUTOTUNE)
+    val_ds = val_ds.batch(batch_size).prefetch(tf.data.AUTOTUNE)
+    train_ds.class_indices = class_indices
+    return train_ds, val_ds
+
+def gated_conv(x, strides):
+    c = layers.DepthwiseConv2D(3, strides=strides, padding='same', activation='gelu')(x)
+    g = layers.DepthwiseConv2D(3, strides=strides, padding='same', activation='sigmoid')(x)
+    return layers.Multiply()([c, g])
+
+def multi_conv(x, strides):
+    convs = [layers.DepthwiseConv2D(kernel_size=k, strides=strides, padding='same')(x) for k in [1, 3, 5, 7]]
+    x = layers.Concatenate()(convs)
+    x = layers.BatchNormalization()(x)
+    return layers.Activation('gelu')(x)
+
+def se_block(x, reduction):
+    filters = x.shape[-1]
+    s = layers.Conv2D(filters // reduction, 1, padding='same', activation='gelu')(x)
+    s = layers.BatchNormalization()(s)
+    s = layers.Conv2D(filters, 1, padding='same', activation='sigmoid')(s)
+    s = layers.BatchNormalization()(s)
+    return layers.multiply([x, s])
+
+def build_model(input_shape, num_classes):
+    i = layers.Input(shape=input_shape)
+    x = multi_conv(i, strides=1)
+    x = multi_conv(x, strides=2)
+    x = gated_conv(x, strides=2)
+    x = multi_conv(x, strides=2)
+    x = multi_conv(x, strides=2)
+    x = multi_conv(x, strides=2)
+    x = se_block(x, reduction=2)
+    x = layers.GlobalAveragePooling2D()(x)
+    o = layers.Dense(num_classes, activation='softmax', dtype='float32')(x)
+    return models.Model(i, o)
+
+def lr_scheduler(epoch, lr):
+    return 1e-3 * (0.7 ** epoch)
+
+class CustomModelCheckpoint(callbacks.Callback):
+    def on_epoch_end(self, epoch, logs=False):
+        models.save_model(self.model, f"PYAS_Model_Epoch_{epoch + 1}.h5")
+        spec = (tf.TensorSpec((None,) + train_ds.image_shape, tf.float32),)
+        model_proto, _ = tf2onnx.convert.from_keras(self.model, input_signature=spec, opset=18)
+        with open(f"PYAS_Model_Epoch_{epoch + 1}.onnx", "wb") as f:
+            f.write(model_proto.SerializeToString())
+
+train_ds, val_ds = load_dataset(r'./Image_File', r'./Image_File')
+for imgs, _ in train_ds.take(1):
+    train_ds.image_shape = imgs.shape[1:]
+    break
+strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
+with strategy.scope():
+    try:
+        model = models.load_model('PYAS_Model_Epoch_0.h5')
+        print("Load model from disk")
+    except:
+        print("Creating a new model")
+        model = build_model(train_ds.image_shape, len(train_ds.class_indices))
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+model.fit(train_ds, epochs=30, callbacks=[CustomModelCheckpoint(), callbacks.LearningRateScheduler(lr_scheduler)], validation_data=val_ds)
+input("Training complete.")

Extens/Repair/Repair_Regedit.reg

@@ -1,117 +0,0 @@
-Windows Registry Editor Version 5.00
-
-; Repair system restrictions
-[HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\MMC]
-; NoControlPanel
-"NoControlPanel"=dword:00000000
-; NoDrives
-"NoDrives"=dword:00000000
-; NoFileMenu
-"NoFileMenu"=dword:00000000
-; NoFind
-"NoFind"=dword:00000000
-; NoRealMode
-"NoRealMode"=dword:00000000
-; NoRecentDocsMenu
-"NoRecentDocsMenu"=dword:00000000
-; NoSetFolders
-"NoSetFolders"=dword:00000000
-; NoSetFolderOptions
-"NoSetFolderOptions"=dword:00000000
-; NoViewOnDrive
-"NoViewOnDrive"=dword:00000000
-; NoClose
-"NoClose"=dword:00000000
-; NoRun
-"NoRun"=dword:00000000
-; NoDesktop
-"NoDesktop"=dword:00000000
-; NoLogOff
-"NoLogOff"=dword:00000000
-; NoFolderOptions
-"NoFolderOptions"=dword:00000000
-; RestrictRun
-"RestrictRun"=dword:00000000
-; NoViewContextMenu
-"NoViewContexMenu"=dword:00000000
-; HideClock
-"HideClock"=dword:00000000
-; NoStartMenuMorePrograms
-"NoStartMenuMorePrograms"=dword:00000000
-; NoStartMenuMyGames
-"NoStartMenuMyGames"=dword:00000000
-; NoStartMenuMyMusic
-"NoStartMenuMyMusic"=dword:00000000
-; DisableCMD
-"DisableCMD"=dword:00000000
-; NoWinKeys
-"NoWinKeys"=dword:00000000
-; StartMenuLogOff
-"StartMenuLogOff"=dword:00000000
-; NoSimpleNetlDList
-"NoSimpleNetlDList"=dword:00000000
-; NoLowDiskSpaceChecks
-"NoLowDiskSpaceChecks"=dword:00000000
-; DisableLockWorkstation
-"DisableLockWorkstation"=dword:00000000
-; Restrict_Run
-"Restrict_Run"=dword:00000000
-; DisableTaskMgr
-"DisableTaskMgr"=dword:00000000
-; DisableRegistryTools
-"DisableRegistryTools"=dword:00000000
-; DisableChangePassword
-"DisableChangePassword"=dword:00000000
-; Wallpaper
-"Wallpaper"=dword:00000000
-; NoComponents
-"NoComponents"=dword:00000000
-; NoAddingComponents
-"NoAddingComponents"=dword:00000000
-; NoStartMenuPinnedList
-"NoStartMenuPinnedList"=dword:00000000
-; NoActiveDesktop
-"NoActiveDesktop"=dword:00000000
-; NoSetActiveDesktop
-"NoSetActiveDesktop"=dword:00000000
-; NoActiveDesktopChanges
-"NoActiveDesktopChanges"=dword:00000000
-; NoChangeStartMenu
-"NoChangeStartMenu"=dword:00000000
-; NoFavoritesMenu
-"NoFavoritesMenu"=dword:00000000
-; NoRecentDocsHistory
-"NoRecentDocsHistory"=dword:00000000
-; NoSetTaskbar
-"NoSetTaskbar"=dword:00000000
-; NoSMHelp
-"NoSMHelp"=dword:00000000
-; NoTrayContextMenu
-"NoTrayContextMenu"=dword:00000000
-; NoViewContextMenu
-"NoViewContextMenu"=dword:00000000
-; NoManageMyComputerVerb
-"NoManageMyComputerVerb"=dword:00000000
-; NoWindowsUpdate
-"NoWindowsUpdate"=dword:00000000
-; ClearRecentDocsOnExit
-"ClearRecentDocsOnExit"=dword:00000000
-; NoStartMenuNetworkPlaces
-"NoStartMenuNetworkPlaces"=dword:00000000
-
-; Repair Image File Execution Options
-[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options]
-; (Delete all subkeys here if necessary)
-
-; Repair executable file icon
-[HKEY_CLASSES_ROOT\exefile\DefaultIcon]
-@="%1"
-[HKEY_LOCAL_MACHINE\SOFTWARE\Classes\exefile\DefaultIcon]
-@="%1"
-
-; Repair executable file type
-[HKEY_CLASSES_ROOT\.exe]
-@="exefile"
-[HKEY_CLASSES_ROOT\exefile\shell\open\command]
-@="\"%1\" %*"
-

Extens/Repair/Repair_System.bat

@@ -1 +0,0 @@
-sfc /scannow

Extens/Tools/Tools.txt

@@ -1 +0,0 @@
-None

PYAS_Config.py

@@ -1,10 +1,11 @@
 translate_dict = {
-    "zh_TW": {"已开启":"已開啟","已关闭":"已關閉","On":"已開啟","Off":"已關閉"},
-    "zh_CN": {"已開啟":"已开启","已關閉":"已关闭","On":"已开启","Off":"已关闭",
+    "traditional_switch": {"开启":"開啟","关闭":"關閉","On":"開啟","Off":"關閉"},
+
+    "simplified_switch": {"開啟":"开启","關閉":"关闭","On":"开启","Off":"关闭",
               "錯誤":"错误","發現":"发现","關閉":"关闭","裝置":"设备","記憶體":"内存",
               "軟體":"软件","檔案":"文件","確定":"确认","設定":"设置","關於":"关于",
               "總數":"总数","註冊":"注册","圖標":"图标","路徑":"路径","網路":"网络",
-              "位元":"字节","靈敏":"敏感","複製":"复制","開啟":"开启","資源回收桶":"资源回收桶",
+              "位元":"字节","靈敏":"敏感","複製":"复制","開啟":"开启","靜態":"静态",
               "訊":"讯","項":"项","掃":"扫","試":"试","黃":"黄","嗎":"吗","防":"保",
               "鑰":"钥","當":"当","後":"后","護":"护","導":"导","區":"区","復":"复",
               "攔":"拦","請":"请","統":"统","刪":"删","進":"进","個":"个","選":"选",
@@ -17,20 +18,23 @@ translate_dict = {
               "強":"强","驅":"驱","為":"为","腳":"脚","單":"单","開":"开","擴":"扩",
               "惡":"恶","據":"据","庫":"库","經":"经","敗":"败","謝":"谢","別":"别",
               "載":"载","誤":"误","報":"报","鏈":"链","竄":"窜","蹤":"踪","與":"与",
-              "異":"异","實":"实","時":"时","彈":"弹","窗":"窗","暫":"暂","訂":"订",},
-    "en_US": {"已開啟":"On","已關閉":"Off","已开启":"On","已关闭":"Off","，":", ",
-              "立即刪除":"Delete Now","安全模式":"Safe Mode","官方網站":"Website ",
+              "異":"异","實":"实","時":"时","彈":"弹","窗":"窗","暫":"暂","訂":"订",
+              "資":"资","產":"产","金":"密","離":"离","規":"规","則":"则","來":"来",
+              "殼":"壳","碼":"码","線":"线",},
+
+    "english_switch": {"開啟":"On","關閉":"Off","开启":"On","关闭":"Off","，":", ",
+              "立即刪除":"Delete Now","安全模式":"Safe Mode","官方網站":"Official Website ",
               "腳本":"Script ","鏈式":"Chain ","記憶體":"Memory ","加載":"Load ",
-              "行為":"Behavior ","可疑":"Suspicious ","勒索":"Ransom ",
-              "許可條款":"License Terms ","惡意":"Malicious","可疑":"Suspicious",
+              "行為":"Behavior ","可疑":"Suspicious ","勒索":"Ransom ","主要":"Main ",
+              "許可條款":"License Terms ","惡意":"Malicious", "開發":"Developers ",
               "啟用":"Enable ","禁用":"Disable ","正在初始化中":"Initializing ",
               "正在掃描":"Scanning ","正在刪除":"Deleting ","正在追蹤":"Tracking ",
               "最小化到":"Minimize To ","靈敏度":"Sensitivity ","正在檢查":"Checking ",
               "確定要":"Sure Want To ","引擎靈敏度":"Sensitivity ","更新":"Update ",
               "白色主題":"White","黃色主題":"Yellow","紅色主題":"Red","語言":"Language ",
-              "藍色主題":"Blue","綠色主題":"Green","自訂主題":"Custom","最新":"Latest ",
+              "藍色主題":"Blue","綠色主題":"Green","自訂主題":"Custom","黑色主題":"Black",
               "特別感謝":"Contributors ","版本資訊":"Information","通訊":"Connect ",
-              "註冊表":"Registry ","退出":"Quit ","檔案":"File ","當前":"Currently ",
+              "註冊":"Registry ","退出":"Quit ","檔案":"File ","當前":"Currently ",
               "狀態":"State ","掃描":"Scan ","工具":"Tools ","防護":"Protect ",
               "失敗":"Failed ","請":"Please ","路徑":"Path ","方式":"Method ",
               "需要":"Need ","病毒":"Virus ","選擇":"Select ","返回":"Back",
@@ -40,27 +44,85 @@ translate_dict = {
               "裝置":"Device ","發現":"Found ","成功":"Success ","清理":"Cleanup ",
               "確定":"OK","執行":"Execute ","警告":"Warning ","使用":"Use ",
               "網路":"Network ","變更":"Change ","總數":"Total","結束":"End ",
-              "選項":"Options ","監控":"Monitor ","連接":"Connect ","可以":"To ",
+              "選項":"Options ","監控":"Monitor ","連接":"Connect ","可以":"Can ",
               "進程":"Process ","攔截":"Block ","引導":"Boot ","刪除":"Delete ",
               "分區":"Partition ","設定":"Setting ","關於":"About","模式":"Mode ",
-              "提高":"Improve ","安全":"Security ","顯色":"Color ","主題":"Themes ",
-              "風險":"Risk ","介面":"GUI ","核心":"Core ","服務":"Services ",
+              "提高":"Improve ","安全":"Security ","顯色":"Color ","顏色":"Color ",
+              "主題":"Theme ", "回收":"Collect ", "無用":"Useless ","導入":"Import ",
+              "風險":"Risk ","介面":"UI ","核心":"Core ","服務":"Services ",
               "管理":"Manage","所有":"All ","您":"You ","取消":"Cancel ","未":"Not ",
               "垃圾":"Junk ","位元":"byte ","本機":"Computer ","停止":"Stop ",
               "到":"To ","白名單":"White List ","軟體":"Software ","正在":"",
               "增加":"Add ","移除":"Delete ","項目":"Items ","生成":"Create ","稱":"",
-              "複製":"Copy ","開啟":"Open ","此":"This ","並":"And ","全盤":"Full ",
-              "已經在":"Already ","數據庫":"Database ","誤報":"False Positive ",
+              "複製":"Copy ","此":"This ","並":"And ","全盤":"Full ","最新":"Latest ",
+              "已存在":"Already ","數據庫":"Database ","誤報":"False Positive ",
               "高":"High ","新":"New ","和":"And ","可能會":"Possible ","會":"Can ",
               "日誌":"Log","已":"Has ","彈窗":"Window ","要":"To ","驅動":"Driver ",
-              "的":"","顯示":"","個":"","嗎":"","中":"","了":"","行":"","立即":"",
+              "的":"","顯示":"","個":"","嗎":"","了":"","行":"","立即":"","規則":"Rule ",
               "密鑰":"Key ","檢測":"Detect ","製作":"Maker ","運行":"Running",
               "擴展":"Extension ","套件":"Kit ","自動":"Auto ","上報":"Report ",
               "分析":"Analysis ","自身":"Self ","其他":"Other ","配置":"Config ",
-              "丟失":"Lost ","網站":"Website ","不兼容":"Not Compatible ",
+              "丟失":"Lost ","網站":"Website ","不兼容":"Not Compatible ","列表":"List ",
               "竄改":"Tamper ","第三方":"Other ","實時":"Real Time ","查看":"View ",
-              "指定":"Specify ","清空":"Empty ","資源回收桶":"Recycle Bin ",
-              "與":"With ","當":"When ","功能":"Function ","異常":"Abnormal ",
+              "指定":"Specify ","清空":"Empty ","資源回收桶":"Recycle Bin ", "無法":"Cannot ",
+              "與":"With ","當":"When ","功能":"Function ","異常":"Abnormal ", "來":"To ",
               "重置":"Reset ","窗口":"Window ","暫存":"Temp ","自訂":"Custom ",
               "不支持":"Not Support ","共":"Total ","秒":"Seconds ","耗時":"Takes ",
-              "時":"","  ":" "}}
+              "沙箱":"Sandbox ", "虛擬":"Virtual ", "隔離":"Quarantine ", "時":"",
+              "建立":"Create ","環境":"Environment ","沙箱":"Sandbox ","化":"", "表":"",
+              "達":"Reached ", "上限":"Limit ", "區":"", "金鑰":"Key ", "產品":"Product ",
+              "托管":"CLR ","殼碼":"Shellcode ","線程":"Thread ","注入":"Inject ",
+              "前往":"Go ","檢查":"Check ","可用":"Available ","靜態":"Static ", "  ":" "},
+
+    "white_switch": {
+        "_white": "_black",},
+    "red_switch": {
+        "235, 235, 235": "255, 235, 235",
+        "245, 245, 245": "255, 225, 225",
+        "255, 255, 255": "255, 245, 245",
+        "240, 240, 240": "255, 220, 220",
+        "250, 250, 250": "255, 210, 210",
+        "220, 255, 220": "255, 220, 220",
+        "210, 255, 210": "255, 210, 210",
+        "_white": "_black"
+    },
+    "yellow_switch": {
+        "235, 235, 235": "255, 255, 235",
+        "245, 245, 245": "255, 255, 225",
+        "255, 255, 255": "255, 255, 245",
+        "240, 240, 240": "255, 255, 220",
+        "250, 250, 250": "255, 255, 210",
+        "220, 255, 220": "255, 255, 220",
+        "210, 255, 210": "255, 255, 210",
+        "_white": "_black"
+    },
+    "green_switch": {
+        "235, 235, 235": "235, 255, 235",
+        "245, 245, 245": "225, 255, 225",
+        "255, 255, 255": "245, 255, 245",
+        "240, 240, 240": "220, 255, 220",
+        "250, 250, 250": "210, 255, 210",
+        "_white": "_black"
+    },
+    "blue_switch": {
+        "235, 235, 235": "235, 235, 255",
+        "245, 245, 245": "225, 225, 255",
+        "255, 255, 255": "245, 245, 255",
+        "240, 240, 240": "220, 220, 255",
+        "250, 250, 250": "210, 210, 255",
+        "220, 255, 220": "220, 220, 255",
+        "210, 255, 210": "210, 210, 255",
+        "_white": "_black"
+    },
+    "black_switch": {
+        "255, 255, 255": "55, 55, 55", 
+        "245, 245, 245": "75, 75, 75",
+        "235, 235, 235": "65, 65, 65", 
+        "240, 240, 240": "80, 80, 80",
+        "250, 250, 250": "90, 90, 90",
+        "220, 255, 220": "80, 80, 80",
+        "210, 255, 210": "90, 90, 90",
+        "0, 0, 0": "255, 255, 255",
+        "_black": "_white"
+    }
+}

PYAS_Engine.py

@@ -1,154 +1,150 @@
-import os, sys, time, json, yara, numpy
-import chardet, pefile, onnxruntime
+import os, yara, numpy, locale, pefile, onnxruntime
 from PIL import Image
 
-class YRScan:
+class rule_scanner:
     def __init__(self):
         self.rules = {}
         self.network = []
 
-    def load_rules(self, file_path):
-        try:
-            ftype = os.path.splitext(file_path)[-1].lower()
-            if ftype in [".yara", ".yar"]:
-                self.rules[file_path] = yara.compile(file_path)
-            elif ftype in [".yc", ".yrc"]:
-                self.rules[file_path] = yara.load(file_path)
-            elif ftype in [".ip", ".ips"]:
-                with open(file_path, "r") as f:
-                    self.network += [l.strip() for l in f.readlines()]
-        except Exception as e:
-            print(e)
+    def load_path(self, path):
+        for root, _, files in os.walk(path):
+            for file in files:
+                self.load_file(os.path.join(root, file))
 
-    def yr_scan(self, file_path):
+    def load_file(self, file):
+        ext = os.path.splitext(file)[1].lower()
         try:
-            if isinstance(file_path, str):
-                with open(file_path, "rb") as f:
-                    file_path = f.read()
-            for name, rules in self.rules.items():
-                matchs_rules = rules.match(data=file_path)
-                if matchs_rules:
-                    label = str(matchs_rules[0]).split("_")[0]
-                    level = str(matchs_rules[0]).split("_")[-1]
+            if ext in ('.yara', '.yar'):
+                self.rules[file] = yara.compile(file)
+            elif ext in ('.yc', '.yrc'):
+                self.rules[file] = yara.load(file)
+            elif ext in ('.ip', '.txt'):
+                with open(file, "r", encoding="utf-8", errors="ignore") as f:
+                    self.network.extend(line.strip() for line in f if line.strip())
+        except Exception:
+            return False
+
+    def yara_scan(self, file_path):
+        try:
+            data = open(file_path, "rb").read() if isinstance(file_path, str) else file_path
+            for rules in self.rules.values():
+                matches = rules.match(data=data)
+                if matches:
+                    rule_name = str(matches[0])
+                    label = rule_name.split("_")[0]
+                    level = rule_name.split("_")[-1]
                     return f"Rules/{label}", level
             return False, False
-        except Exception as e:
+        except Exception:
             return False, False
 
-class DLScan:
+class model_scanner:
+    SHELLS = {
+        "0","1","2","3","4","5","6","7","8","9","cry","tvm","dec","enc","vmp","upx","aes","lzma","press","pack",
+        "enigma","protect","secur","asmstub","base","bss","clr_uef","cursors","transit","trs_age","engine","fio",
+        "fothk","h~;","icapsec","malloc_h","miniex","mssmixer","ndr64","nsys_wr","obr","wow","wow64svc","wpp_sf",
+        "pad","pgae","poolmi","proxy","qihoo","retpol","uedbg","tracesup","rwexec","rygs","s:@","sanontcp","segm",
+        "test","res","wisevec","viahwaes","orpc","nep","ace","extjmp","no_bbt","data","page","hexpthk"}
+
     def __init__(self):
         self.models = {}
-        self.valid_interpolations = {
-        "box": Image.Resampling.BOX,
-        "none": Image.Resampling.NEAREST,
-        "bilinear": Image.Resampling.BILINEAR,
-        "hamming": Image.Resampling.HAMMING,
-        "bicubic": Image.Resampling.BICUBIC,
-        "lanczos": Image.Resampling.LANCZOS,
-        "nearest": Image.Resampling.NEAREST}
+        self.suffix = {".com", ".exe", ".dll", ".sys", ".scr", ".bat", ".cmd", ".ps1", ".vbs", ".wsf"}
+        self.labels = ["Pefile/White", "Script/White", "Pefile/General", "Script/General"]
+        self.detect = {"Pefile/General", "Script/General"}
+        self.resize = (224, 224)
 
-        shell_section = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", 
-        "cry", "tvm", "dec", "enc", "vmp", "upx", "aes", "lzma", "press", 
-        "pack", "enigma", "protect", "secur"]
-        unknown_section = ["asmstub", "base", "bss", "clr_uef", "cursors", 
-        "engine", "fio", "fothk", "h~;", "icapsec", "malloc_h", "miniex", 
-        "mssmixer", "ndr64", "nsys_wr", "obr", "wow", "wow64svc", "wpp_sf",
-        "pad", "pgae", "poolmi", "proxy", "qihoo", "res", "retpol", "uedbg",
-        "rwexec", "rygs", "s:@", "sanontcp", "segm", "test", "tracesup",
-        "transit", "trs_age", "wisevec"]
-        unimportant_section = ["viahwaes", "orpc", "nep", "ace", "extjmp", 
-        "no_bbt", "data", "page", "hexpthk"]
-        self.shells = shell_section + unimportant_section + unknown_section
+    def load_path(self, path):
+        for root, _, files in os.walk(path):
+            for file in files:
+                self.load_file(os.path.join(root, file))
 
-    def load_model(self, file_path):
+    def load_file(self, file):
+        if file.lower().endswith('.onnx'):
+            try:
+                self.models[file] = onnxruntime.InferenceSession(file)
+            except Exception:
+                return False
+
+    def is_valid_suffix(self, file_path):
+        return os.path.splitext(file_path)[-1].lower() in self.suffix
+
+    def model_scan(self, file_path):
         try:
-            ftype = os.path.splitext(file_path)[-1].lower()
-            if ftype in [".json", ".txt"]:
-                with open(file_path, 'r') as f:
-                    self.class_names = json.load(f)
-            elif ftype in [".onnx"]:
-                self.models[file_path] = onnxruntime.InferenceSession(file_path)
-            self.labels = self.class_names['Labels']
-            self.detect = self.class_names['Detect']
-            self.values = self.class_names['Values']
-            self.resize = self.class_names['Resize']
-            self.suffix = self.class_names['Suffix']
-        except Exception as e:
-            pass
+            if not self.models or not self.is_valid_suffix(file_path):
+                return False, False
 
-    def dl_scan(self, file_path):
-        try:
-            batch_images = []
-            for section, file_data in self.get_type(file_path).items():
-                image_data = self.preprocess_image(file_data, tuple(self.resize))
-                image_array = numpy.asarray(image_data).astype('float32') / 255.0
-                if len(image_array.shape) == 2:
-                    image_array = numpy.expand_dims(image_array, axis=-1)
-                batch_images.append(image_array)
-            batch_results = [{label: [] for label in self.labels} for _ in range(len(batch_images))]
-            batch_images = numpy.stack(batch_images, axis=0)
-            for model_name, model in self.models.items():
-                input_name = model.get_inputs()[0].name
-                predictions = model.run(None, {input_name: batch_images})[0]
-                for i, pre_answers in enumerate(predictions):
-                    for k, score in enumerate(pre_answers):
-                        batch_results[i][self.labels[k].strip()].append(score)
-            final_results = []
-            for result in batch_results:
-                label_percentage = {label: (sum(scores) / len(scores)) * 100
-                for label, scores in result.items()}
-                best_label, best_confidence = max(label_percentage.items(), key=lambda x: x[1])
-                final_results.append((best_label, int(best_confidence)))
-                if best_label in self.detect:
-                    return best_label, int(best_confidence)
+            model = next(iter(self.models.values()))
+            shape = model.get_inputs()[0].shape
+            channels = shape[-1] if shape[-1] in (1, 3) else (shape[1] if shape[1] in (1, 3) else 1)
+
+            file_sections = self.get_type(file_path)
+            if not file_sections:
+                return False, False
+
+            batch_images = [self.preprocess_image(data, self.resize, channels) for data in file_sections.values()]
+            arr = numpy.stack([numpy.asarray(img).astype('float32') / 255.0 for img in batch_images])
+            if arr.ndim == 3:
+                arr = arr[..., None]
+            if shape[-1] in (1, 3):
+                model_input = arr
+            elif shape[1] in (1, 3):
+                model_input = arr.transpose(0, 3, 1, 2)
+            else:
+                return False, False
+
+            input_name = model.get_inputs()[0].name
+            predictions = model.run(None, {input_name: model_input})[0]
+
+            label_scores = {label: [] for label in self.labels}
+            for pre_answers in predictions:
+                for k, score in enumerate(pre_answers):
+                    label_scores[self.labels[k]].append(score)
+            label_percentage = {label: (sum(scores)/len(scores))*100 for label, scores in label_scores.items() if scores}
+            if not label_percentage:
+                return False, False
+            best_label, best_conf = max(label_percentage.items(), key=lambda x: x[1])
+            if best_label in self.detect:
+                return best_label, int(best_conf)
             return False, False
-        except Exception as e:
+        except Exception:
             return False, False
 
-    def preprocess_image(self, file_data, target_size):
-        width, height, channels, interpolation = target_size
+    def preprocess_image(self, file_data, size, channels=1):
+        width, height = size
         wah = int(numpy.ceil(numpy.sqrt(len(file_data) / channels)))
-        array_data = numpy.frombuffer(file_data, dtype=numpy.uint8)
-        expected_size = wah * wah * channels
-        image_array = numpy.zeros((expected_size,), dtype=numpy.uint8)
-        image_array[:len(file_data)] = array_data
+        arr = numpy.frombuffer(file_data, dtype=numpy.uint8)
+        img = numpy.zeros(wah*wah*channels, dtype=numpy.uint8)
+        img[:len(file_data)] = arr
         if channels == 1:
-            reshape = image_array.reshape((wah, wah))
-            image = Image.fromarray(reshape, 'L')
-        elif channels == 3:
-            reshape = image_array.reshape((wah, wah, 3))
-            image = Image.fromarray(reshape, 'RGB')
-        if width == 0 and height == 0:
-            return image
-        i = self.valid_interpolations.get(interpolation.lower(), Image.BILINEAR)
-        return image.resize((width, height), i)
+            image = Image.fromarray(img.reshape((wah, wah)), 'L')
+        else:
+            image = Image.fromarray(img.reshape((wah, wah, channels)), 'RGB')
+        return image.resize((width, height), Image.Resampling.NEAREST)
 
-    def is_text_file(self, content, sample_size):
-        try:
-            raw_data = content[:sample_size]
-            encoding = chardet.detect(raw_data)["encoding"]
-            if encoding in ["ascii", "utf-8", "utf-8-sig"]:
-                if raw_data.decode(encoding):
-                    return True
-            return False
-        except:
+    def is_text_file(self, content, sample_size=1024):
+        raw = content[:sample_size]
+        if not raw:
             return False
+        text_char = set(range(32, 127)) | {9, 10, 13}
+        nontext = sum(b not in text_char for b in raw)
+        return nontext / len(raw) < 0.15
 
     def get_type(self, file_path):
         match_data = {}
-        ftype = os.path.splitext(file_path)[-1].lower()
-        if ftype in self.suffix:
-            try:
-                with pefile.PE(file_path, fast_load=True) as pe:
-                    for section in pe.sections:
-                        name = section.Name.rstrip(b'\x00').decode('latin1')
-                        if (section.Characteristics & 0x00000020 and not
-                        any(s in name.lower() for s in self.shells)):
-                            print(name)
-                            match_data[name] = section.get_data()
-            except:
-                with open(file_path, 'rb') as f:
-                    file_content = f.read()
-                if self.is_text_file(file_content, 1024):
-                    match_data[ftype] = file_content
+        if not self.is_valid_suffix(file_path):
+            return match_data
+        try:
+            with pefile.PE(file_path, fast_load=True) as pe:
+                sec = pe.OPTIONAL_HEADER.DATA_DIRECTORY[pefile.DIRECTORY_ENTRY['IMAGE_DIRECTORY_ENTRY_SECURITY']]
+                if bool(sec.VirtualAddress and sec.Size):
+                    return {}
+                for section in pe.sections:
+                    name = section.Name.rstrip(b'\x00').decode('latin1').lower()
+                    if (section.Characteristics & 0x00000020) and name not in self.SHELLS:
+                        match_data[name] = section.get_data()
+        except pefile.PEFormatError:
+            with open(file_path, 'rb') as f:
+                file_content = f.read()
+            if self.is_text_file(file_content):
+                match_data[os.path.splitext(file_path)[-1].lower()] = file_content
         return match_data

PYAS_Suffixes.py

@@ -1,10 +0,0 @@
-file_types = [
-    ".exe",".dll",".sys",".com",".scr",
-    ".zip",".7z",".rar",".tar",".gz",
-    ".js",".bat",".cmd",".ps1",".vbs",
-    ".ppt",".pptx",".wps",".txt",".rtf",
-    ".pdf",".xls",".xlsx",".doc",".docx",
-    ".jpg",".jpeg",".png",".webp",".gif",
-    ".mp3",".wav",".aac",".ogg",".flac",
-    ".mp4",".avi",".mov",".wmv",".mkv",
-    ".aux",".cur",".mui",".ttf",".efi"]

PYAS_Version.py

@@ -2,7 +2,7 @@ import pyinstaller_versionfile
 
 pyinstaller_versionfile.create_versionfile(
 output_file="versionfile.txt",
-version='3.3.0',
+version='3.3.3',
 company_name="PYAS Security",
 file_description="Python Antivirus Software",
 internal_name="PYAS",

README.md

@@ -1,76 +1,72 @@
-# PYAS (Python Antivirus Software)
+# PYAS
+
+Python antivirus software uses deep learning and behavioral monitoring to block threats!
 
 ![PYAS_UI](https://github.com/user-attachments/assets/68765836-7272-482f-b8cd-d8ba728d88ab)
 
 ## Readme Language
 
+English : https://github.com/87owo/PYAS/blob/main/README.md
+
 繁體中文 : https://github.com/87owo/PYAS/blob/main/README_zh_TW.md
 
 简体中文 : https://github.com/87owo/PYAS/blob/main/README_zh_CN.md
 
 ## Requirements
 
+Python 3.10 is recommended. Other Python versions may require different pip commands.
+
 ```
-pip install chardet==5.2.0
-pip install numpy==1.26.3
+pip install numpy==1.26.4
 pip install onnxruntime==1.18.1
 pip install pefile==2023.2.7
-pip install pillow==10.4.0
-pip install pyperclip==1.8.2
-pip install PyQt5==5.15.11
-pip install requests==2.32.3
-pip install yara-python==4.5.1
+pip install Pillow==11.0.0
+pip install pyperclip==1.9.0
+pip install PySide6==6.9.1
+pip install requests==2.32.4
+pip install yara-python==4.5.4
 ```
 
 ## File Information
 
+The following lists the storage locations of all relevant code and other related documents.
+
 ```
 PYAS/
-├── Driver/ (Protect driver and Microsoft runtime)
-│   └── ...
+├── Engine/
+│   ├── Models/              # Directory for ONNX AI models
+│   └── Rules/               # Directory for YARA and network rules
 │
-├── Engine/ (Deep learn model and Yara database)
-│   └── ...
+├── Plugins/
+│   └── Filter/              # Windows kernel driver for security protection
 │
-├── Exten/ (Exten tools and System repair tools)
-│   └── ...
-│
-├── PYAS.py (Main PYAS program with qtui interaction and protect function)
-├── PYAS_Engine.py (Deep learning predict and yara rules file scanner)
-├── PYAS_Suffixes.py (including scan file suffixes and common suffixes)
-├── PYAS_Interface.py (Qt designer ui, must be matched with the main program)
-├── PYAS_Resource.py (status pictures, icon packages, and button icons)
-├── PYAS_Language.py (Traditional Chinese, Simplified Chinese and English)
-├── PYAS_Version.py (file information, version information, original name)
-└── ...
+├── PYAS.py                  # Main application and GUI interface
+├── PYAS_Config.py           # Configuration handling and global parameters
+├── PYAS_Engine.py           # YARA and ONNX Scanning engine
+├── PYAS_Interface.py        # User interface definitions and widget logic
+├── PYAS_Resource.py         # Static picture resource management
+├── PYAS_Version.py          # Packaging and version info
+└── ...                      # Other supplementary folders and files
 ```
 
-## Official Website
+## Packaged Releases
 
-https://pyantivirus.wixsite.com/pyas
-
-https://github.com/87owo/PYAS
-
-## Microsoft Runtime
-
-https://github.com/87owo/Microsoft_Runtime/releases
-
-## PYAS Driver
-
-https://github.com/0sha0/PYAS_Protection
-
-## MIT license
-
-https://github.com/87owo/PYAS/blob/main/LICENSE.md
+Packaged Releases Download: https://github.com/87owo/PYAS/releases
 
 ## Support System
 
-Windows 8.1, 10, 11 (64-bit), Ram 500MB, Rom 200MB or higher
+| Config    | Permissions   | System version | Available memory | Available storage |
+|-----------|---------------|----------------|------------------|-------------------|
+| Minimum   | Administrator | Windows 10     | 200MB            | 100MB             |
+| Recommend | Administrator | Windows 10     | 300MB            | 200MB             |
+| Optimal   | Administrator | Windows 11     | 500MB            | 200MB             |
 
-Other systems may experience software malfunctions or crash
+## Official Website
 
-## Special Thanks
+Official Website : https://pyantivirus.wixsite.com/pyas
 
-Wix, mtkiao129, 0sha0, AV-T Team of LisectGroup
+Open Source Website : https://github.com/87owo/PYAS
 
-Copyright© 2020~2025 PYAS Security By 87owo
+## Project License
+
+https://github.com/87owo/PYAS/blob/main/LICENSE.md

README_zh_CN.md

@@ -1,70 +1,72 @@
-# PYAS (Python 杀毒软件)
+# PYAS
+
+以 Python 开发的杀毒软件，结合深度学习与行为监控来阻挡威胁！
 
 ![PYAS_UI](https://github.com/user-attachments/assets/68765836-7272-482f-b8cd-d8ba728d88ab)
 
-## 安装要求
+## 说明语言
+
+English : https://github.com/87owo/PYAS/blob/main/README.md
+
+繁體中文 : https://github.com/87owo/PYAS/blob/main/README_zh_TW.md
+
+简体中文 : https://github.com/87owo/PYAS/blob/main/README_zh_CN.md
+
+## 安装需求
+
+建议使用 Python 3.10。其他版本的 Python 可能需要不同的 pip 指令。
 
 ```
-pip install chardet==5.2.0
-pip install numpy==1.26.3
+pip install numpy==1.26.4
 pip install onnxruntime==1.18.1
 pip install pefile==2023.2.7
-pip install pillow==10.4.0
-pip install pyperclip==1.8.2
-pip install PyQt5==5.15.11
-pip install requests==2.32.3
-pip install yara-python==4.5.1
+pip install Pillow==11.0.0
+pip install pyperclip==1.9.0
+pip install PySide6==6.9.1
+pip install requests==2.32.4
+pip install yara-python==4.5.4
 ```
 
-## 文件信息
+## 文件讯息
+
+以下列出所有相关代码与文件的存放位置。
 
 ```
 PYAS/
-├── Driver/ (驱动保护和微软运行库)
-│   └── ...
+├── Engine/
+│   ├── Models/              # ONNX 深度学习模型目录
+│   └── Rules/               # YARA 与网络规则目录
 │
-├── Engine/ (深度学习和规则数据库)
-│   └── ...
+├── Plugins/
+│   └── Filter/              # 用于安全防护的 Windows 核心驱动
 │
-├── Exten/ (扩展功能与系统修复工具)
-│   └── ...
-│
-├── PYAS.py (PYAS主程序含界面交互与保护功能)
-├── PYAS_Engine.py (深度学习预测和规则扫描器)
-├── PYAS_Suffixes.py (获取扫描或事件文件的后缀)
-├── PYAS_Interface.py (搭配主程序的Qt设计介面)
-├── PYAS_Resource.py (状态图片和图标和按钮图片)
-├── PYAS_Language.py (繁体中文和简体中文和英文翻译)
-├── PYAS_Version.py (版本和讯息名称和打包文件名)
-└── ...
+├── PYAS.py                  # 主程式与图形界面 (GUI)
+├── PYAS_Config.py           # 组态处理与全域参数
+├── PYAS_Engine.py           # YARA 与 ONNX 扫描引擎
+├── PYAS_Interface.py        # 使用者介面定义与元件逻辑
+├── PYAS_Resource.py         # 静态图片资源管理
+├── PYAS_Version.py          # 打包与版本资讯
+└── ...                      # 其他补充文件夹与文件
 ```
 
-## 官方网页
+## 安装版本
 
-https://pyantivirus.wixsite.com/pyas
+打包发行版下载：https://github.com/87owo/PYAS/releases
 
-https://github.com/87owo/PYAS
+## 系统需求
 
-## 微软运行库
+| 配置需求 | 权限需求    | 系统需求        | 可用內存 | 可用储存空间 |
+| ---- | ------- | ----------- | ----- | ------ |
+| 最低 | 系统管理员 | Windows 10  | 200MB | 100MB  |
+| 建议 | 系统管理员 | Windows 10  | 300MB | 200MB  |
+| 最佳 | 系统管理员 | Windows 11  | 500MB | 200MB  |
 
-https://github.com/87owo/Microsoft_Runtime/releases
+## 官方网站
 
-## 驱动程序
+官方网站 : https://pyantivirus.wixsite.com/pyas
 
-https://github.com/0sha0/PYAS_Protection
+开源网站 : https://github.com/87owo/PYAS
 
-## 开源协议
+## 授权条款
 
 https://github.com/87owo/PYAS/blob/main/LICENSE.md
-
-## 支援系统
-
-Windows 8.1, 10, 11 (64-bit), 内存 500MB, 存储 200MB 或更高
-
-其他系统或版本可能会遇到软体功能故障或程序崩溃
-
-## 特别感谢
-
-Wix, mtkiao129, 0sha0, AV-T Team of LisectGroup
-
-Copyright© 2020~2025 PYAS Security By 87owo

README_zh_TW.md

@@ -1,70 +1,72 @@
-# PYAS (Python 防毒軟體)
+# PYAS
+
+以 Python 開發的防毒軟體，結合深度學習與行為監控來阻擋威脅！
 
 ![PYAS_UI](https://github.com/user-attachments/assets/68765836-7272-482f-b8cd-d8ba728d88ab)
 
-## 安裝要求
+## 說明語言
+
+English : https://github.com/87owo/PYAS/blob/main/README.md
+
+繁體中文 : https://github.com/87owo/PYAS/blob/main/README_zh_TW.md
+
+简体中文 : https://github.com/87owo/PYAS/blob/main/README_zh_CN.md
+
+## 安裝需求
+
+建議使用 Python 3.10。其他版本的 Python 可能需要不同的 pip 指令。
 
 ```
-pip install chardet==5.2.0
-pip install numpy==1.26.3
+pip install numpy==1.26.4
 pip install onnxruntime==1.18.1
 pip install pefile==2023.2.7
-pip install pillow==10.4.0
-pip install pyperclip==1.8.2
-pip install PyQt5==5.15.11
-pip install requests==2.32.3
-pip install yara-python==4.5.1
+pip install Pillow==11.0.0
+pip install pyperclip==1.9.0
+pip install PySide6==6.9.1
+pip install requests==2.32.4
+pip install yara-python==4.5.4
 ```
 
 ## 檔案資訊
 
+以下列出所有相關程式碼與文件的存放位置。
+
 ```
 PYAS/
-├── Driver/ (驅動防護和微軟運行庫)
-│   └── ...
+├── Engine/
+│   ├── Models/              # ONNX 深度學習模型目錄
+│   └── Rules/               # YARA 與網路規則目錄
 │
-├── Engine/ (深度學習和規則數據庫)
-│   └── ...
+├── Plugins/
+│   └── Filter/              # 用於安全防護的 Windows 核心驅動程式
 │
-├── Exten/ (擴展功能與系統修復工具)
-│   └── ...
-│
-├── PYAS.py (PYAS主程序含介面交互與防護功能)
-├── PYAS_Engine.py (深度學習預測和規則掃描器)
-├── PYAS_Suffixes.py (獲取掃描或事件檔案的副檔名)
-├── PYAS_Interface.py (搭配主程序的Qt設計介面)
-├── PYAS_Resource.py (狀態圖片圖標和按鈕圖片)
-├── PYAS_Language.py (繁體中文和簡體中文和英文翻譯)
-├── PYAS_Version.py (檔案版本和訊息名稱和打包檔名)
-└── ...
+├── PYAS.py                  # 主程式與圖形介面 (GUI)
+├── PYAS_Config.py           # 組態處理與全域參數
+├── PYAS_Engine.py           # YARA 與 ONNX 掃描引擎
+├── PYAS_Interface.py        # 使用者介面定義與元件邏輯
+├── PYAS_Resource.py         # 靜態圖片資源管理
+├── PYAS_Version.py          # 打包與版本資訊
+└── ...                      # 其他補充資料夾與檔案
 ```
 
-## 官方網頁
+## 安裝版本
 
-https://pyantivirus.wixsite.com/pyas
+打包發行版下載：https://github.com/87owo/PYAS/releases
 
-https://github.com/87owo/PYAS
+## 系統需求
 
-## 微軟運行庫
+| 配置需求 | 權限需求    | 系統需求        | 可用記憶體 | 可用儲存空間 |
+| ---- | ------- | ----------- | ----- | ------ |
+| 最低 | 系統管理員 | Windows 10  | 200MB | 100MB  |
+| 建議 | 系統管理員 | Windows 10  | 300MB | 200MB  |
+| 最佳 | 系統管理員 | Windows 11  | 500MB | 200MB  |
 
-https://github.com/87owo/Microsoft_Runtime/releases
+## 官方網站
 
-## 驅動程式
+官方網站 : https://pyantivirus.wixsite.com/pyas
 
-https://github.com/0sha0/PYAS_Protection
+開源網站 : https://github.com/87owo/PYAS
 
-## 開源協議
+## 授權條款
 
 https://github.com/87owo/PYAS/blob/main/LICENSE.md
-
-## 支援系統
-
-Windows 8.1, 10, 11 (64-bit),記憶體 500MB, 磁碟容量 200MB 或更高
-
-其他系統或版本可能會遇到軟體功能故障或程序崩潰
-
-## 特別感謝
-
-Wix, mtkiao129, 0sha0, AV-T Team of LisectGroup
-
-Copyright© 2020~2025 PYAS Security By 87owo