Add files via upload

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.")

PYAS.py

@@ -112,7 +112,7 @@ class MainWindow_Controller(QMainWindow):
             "traditional_switch", "simplified_switch", "english_switch",
         ]
         self.pyas_default = {
-            "version": "3.3.2",
+            "version": "3.3.3",
             "product": "00000-00000-00000-00000-00000",
             "language": "english_switch",
             "theme": "white_switch",
@@ -139,6 +139,7 @@ class MainWindow_Controller(QMainWindow):
             "SHELLCODE_BLOCK": "殼碼行為攔截",
             "THREAD_BLOCK": "線程行為攔截",
             "INJECT_BLOCK": "注入行為攔截",
+            "PROC_BLOCK": "進程行為攔截",
         }
 
 ####################################################################################################
@@ -147,6 +148,7 @@ class MainWindow_Controller(QMainWindow):
         self.file_pyas = self.norm_path(sys.argv[0])
         self.args_pyas = sys.argv[1:]
         self.path_pyas = os.path.dirname(self.file_pyas)
+        self.pid_pyas = int(os.getpid())
 
         self.path_appdata = os.environ.get("APPDATA")
         self.path_name = os.environ.get("USERNAME")
@@ -334,17 +336,20 @@ class MainWindow_Controller(QMainWindow):
             if checked:
                 self.start_daemon_thread(self.protect_system_thread)
         elif name == "driver_switch":
-            if checked:
-                self.install_system_driver()
+            if not checked:
+                self.stop_system_driver()
+            elif self.install_system_driver():
                 self.start_daemon_thread(self.pipe_server_thread)
             else:
-                self.stop_system_driver()
+                self.pyas_config[name] = False
+                self.send_message("驅動防護啟用失敗", "warn", True)
         elif name == "network_switch":
             if checked:
                 self.start_daemon_thread(self.protect_net_thread)
         else:
             self.send_message(f"此功能不支持使用", "info", True)
         self.save_config(self.file_config, self.pyas_config)
+        self.apply_settings()
 
     def init_thread(self):
         self.start_daemon_thread(self.popup_intercept_thread)
@@ -390,9 +395,12 @@ class MainWindow_Controller(QMainWindow):
                         widget.setProperty("_connected_toggle", True)
 
             elif hasattr(widget, "setText"):
-                if not hasattr(widget, "_origin_text"):
-                    widget._origin_text = self.get_widget_text(widget)
-                widget.setText(self.trans(lang, widget._origin_text))
+                if name == "log_text":
+                    pass
+                else:
+                    if not hasattr(widget, "_origin_text"):
+                        widget._origin_text = self.get_widget_text(widget)
+                    widget.setText(self.trans(lang, widget._origin_text))
 
             if hasattr(widget, "setStyleSheet"):
                 if not hasattr(widget, "_origin_style"):

PYAS_Version.py

@@ -2,7 +2,7 @@ import pyinstaller_versionfile
 
 pyinstaller_versionfile.create_versionfile(
 output_file="versionfile.txt",
-version='3.3.2',
+version='3.3.3',
 company_name="PYAS Security",
 file_description="Python Antivirus Software",
 internal_name="PYAS",

README.md

@@ -6,6 +6,8 @@ Python antivirus software uses deep learning and behavioral monitoring to block
 
 ## 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
@@ -42,7 +44,7 @@ PYAS/
 ├── 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 resource management
+├── PYAS_Resource.py         # Static picture resource management
 ├── PYAS_Version.py          # Packaging and version info
 └── ...                      # Other supplementary folders and files
 ```
@@ -55,13 +57,15 @@ Packaged Releases Download: https://github.com/87owo/PYAS/releases
 
 | Config    | Permissions   | System version | Available memory | Available storage |
 |-----------|---------------|----------------|------------------|-------------------|
-| Minimum   | Administrator | Windows 8.1    | 200MB            | 100MB             |
+| Minimum   | Administrator | Windows 10     | 200MB            | 100MB             |
 | Recommend | Administrator | Windows 10     | 300MB            | 200MB             |
 | Optimal   | Administrator | Windows 11     | 500MB            | 200MB             |
 
 ## Official Website
 
-https://github.com/87owo/PYAS
+Official Website : https://pyantivirus.wixsite.com/pyas
+
+Open Source Website : https://github.com/87owo/PYAS
 
 ## Project License
 

README_zh_CN.md

@@ -1,8 +1,20 @@
-# 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 numpy==1.26.4
@@ -15,47 +27,46 @@ 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/
-├── PYAS.py                  # 主程序，提供 PySide6 视觉化操作介面
-├── PYAS_Config.py           # 设定档处理与全域参数管理
-├── PYAS_Engine.py           # 扫描引擎，整合 YARA 规则与 ONNX AI 模型
-├── PYAS_Interface.py        # UI 介面定义与元件交互
-├── PYAS_Resource.py         # 资源档与静态资源管理
-├── PYAS_Version.py          # 程式版本与打包资讯
 ├── Engine/
-│   ├── Models/              # AI 模型（ONNX 格式）存放目录
-│   └── Rules/               # YARA 规则与网路规则存放目录
+│   ├── Models/              # ONNX 深度学习模型目录
+│   └── Rules/               # YARA 与网络规则目录
+│
 ├── Plugins/
-│   └── Filter/              # 驱动程式目录
-│       └── PYAS_Driver.sys  # Windows 驱动程式（系统保护用）
-└── ...                      # 其它辅助资料夹与档案
+│   └── Filter/              # 用于安全防护的 Windows 核心驱动
+│
+├── 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
 
-## 开源协议
+官方网站 : https://pyantivirus.wixsite.com/pyas
+
+开源网站 : https://github.com/87owo/PYAS
+
+## 授权条款
 
 https://github.com/87owo/PYAS/blob/main/LICENSE.md
-
-## 支援系统
-
-Windows 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

@@ -1,8 +1,20 @@
-# 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 numpy==1.26.4
@@ -17,45 +29,44 @@ 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/
-├── PYAS.py                  # 主程式，提供 PySide6 視覺化操作介面
-├── PYAS_Config.py           # 設定檔處理與全域參數管理
-├── PYAS_Engine.py           # 掃描引擎，整合 YARA 規則與 ONNX AI 模型
-├── PYAS_Interface.py        # UI 介面定義與元件交互
-├── PYAS_Resource.py         # 資源檔與靜態資源管理
-├── PYAS_Version.py          # 程式版本與打包資訊
 ├── Engine/
-│   ├── Models/              # AI 模型（ONNX 格式）存放目錄
-│   └── Rules/               # YARA 規則與網路規則存放目錄
+│   ├── Models/              # ONNX 深度學習模型目錄
+│   └── Rules/               # YARA 與網路規則目錄
+│
 ├── Plugins/
-│   └── Filter/              # 驅動程式目錄
-│       └── PYAS_Driver.sys  # Windows 驅動程式（系統保護用）
-└── ...                      # 其它輔助資料夾與檔案
+│   └── Filter/              # 用於安全防護的 Windows 核心驅動程式
+│
+├── 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
 
-## 開源協議
+官方網站 : https://pyantivirus.wixsite.com/pyas
+
+開源網站 : https://github.com/87owo/PYAS
+
+## 授權條款
 
 https://github.com/87owo/PYAS/blob/main/LICENSE.md
-
-## 支援系統
-
-Windows 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