Delete PYAS_Function.7z

PYAS.py

@@ -3,11 +3,14 @@ import hashlib, pefile, socket, msvcrt
 import requests, pyperclip, win32file
 import win32gui, win32api, win32con
 import xml.etree.ElementTree as xmlet
-from PYAS_Function import function_list
-from PYAS_Function_Safe import function_list_safe
+from PYAS_Database import model_dict
+from PYAS_Function_Virus import func_virus
+from PYAS_Function_Safe import func_safe
 from PYAS_Extension import slist, alist
 from PYAS_Language import translate_dict
 from PYAS_Interface import Ui_MainWindow
+from PYML import ListClassifier
+from binascii import hexlify
 from threading import Thread
 from PyQt5.QtWidgets import *
 from PyQt5.QtCore import *
@@ -19,10 +22,11 @@ class MainWindow_Controller(QMainWindow):
         self.setAttribute(Qt.WA_TranslucentBackground)
         self.setWindowFlags(Qt.FramelessWindowHint)
         self.pyas = str(sys.argv[0]).replace("\\", "/")
-        self.pyas_version = "2.9.5"
+        self.pyas_version = "2.9.7"
         self.ui = Ui_MainWindow()
         self.ui.setupUi(self)
         self.init_tray_icon()
+        self.init_data_base()
         self.init_virus_list()
         self.init_state_safe()
         self.init_config_path()
@@ -36,6 +40,10 @@ class MainWindow_Controller(QMainWindow):
         self.show_pyas_ui()
         self.init_threads()
 
+    def init_data_base(self):
+        self.ml = ListClassifier()
+        self.ml.load_model(model_dict)
+
     def init_threads(self):
         self.protect_proc_init()
         self.protect_file_init()
@@ -269,7 +277,7 @@ class MainWindow_Controller(QMainWindow):
         self.ui.Window_Block_Button.setText(self.trans("軟體彈窗攔截"))
         self.ui.Repair_System_Network_Button.setText(self.trans("系統網路修復"))
         self.ui.About_Back.setText(self.trans("返回"))
-        self.ui.PYAS_Version.setText(self.trans(f"PYAS V{self.pyas_version}"))
+        self.ui.PYAS_Version.setText(self.trans(f"PYAS V{self.pyas_version} (ML Engine Beta)"))
         self.ui.GUI_Made_title.setText(self.trans("介面製作:"))
         self.ui.GUI_Made_Name.setText(self.trans("mtkiao"))
         self.ui.Core_Made_title.setText(self.trans("核心製作:"))
@@ -778,14 +786,18 @@ class MainWindow_Controller(QMainWindow):
             file_type = str(f".{file.split('.')[-1]}").lower()
             if self.high_sensitivity and file != self.pyas:
                 if self.api_scan(file):
-                    self.write_scan(self.trans("惡意"),file)
+                    self.write_scan(self.trans("Vir.cloud.md5"),file)
+                elif self.ml_scan(file):
+                    self.write_scan(self.trans("Sus.hdfile.ml"),file)
                 elif self.pe_scan(file):
-                    self.write_scan(self.trans("可疑"),file)
-            elif file_type in slist and self.sign_scan(file):
+                    self.write_scan(self.trans("Sus.pefile.he"),file)
+            elif file_type in slist and file != self.pyas:
                 if self.api_scan(file):
-                    self.write_scan(self.trans("惡意"),file)
+                    self.write_scan(self.trans("Vir.cloud.md5"),file)
+                elif self.ml_scan(file):
+                    self.write_scan(self.trans("Sus.hdfile.ml"),file)
                 elif self.pe_scan(file):
-                    self.write_scan(self.trans("可疑"),file)
+                    self.write_scan(self.trans("Sus.pefile.he"),file)
         except:
             pass
 
@@ -837,16 +849,46 @@ class MainWindow_Controller(QMainWindow):
                         except:
                             pass
             max_vfl = []
-            for vfl in function_list:
+            for vfl in func_virus:
                 QApplication.processEvents()
                 max_vfl.append(len(set(fn)&set(vfl))/len(set(fn)|set(vfl)))
             max_sfl = []
-            for sfl in function_list_safe:
+            for sfl in func_safe:
                 QApplication.processEvents()
                 max_sfl.append(len(set(fn)&set(sfl))/len(set(fn)|set(sfl)))
-            if self.high_sensitivity and max(max_vfl) == 1.0:
-                return True
-            elif max(max_vfl) - max(max_sfl) >= 0.1:
+            if self.high_sensitivity:
+                return max(max_vfl) >= max(max_sfl)
+            elif "_CorExeMain" not in fn:
+                return max(max_vfl) >= max(max_sfl)
+            return False
+        except:
+            return False
+
+    def ml_scan(self, file):
+        try:
+            data = []
+            if os.path.getsize(file) <= 209715200:
+                with open(file, 'rb') as f:
+                    for block in iter(lambda: f.read(3),b''):
+                        QApplication.processEvents()
+                        data.append(str(hexlify(block),'utf-8'))
+            if self.high_sensitivity:
+                return self.ml.predict(data) == 1
+            elif self.sign_scan(file):
+                return self.ml.predict(data) == 1
+            return False
+        except:
+            return False
+
+    def scan_proc(self, value):
+        try:
+            if isinstance(value, int):
+                for entry in psutil.Process(value).memory_maps():
+                    file = entry.path.replace("\\", "/")
+                    if ":/Windows" not in file and ":/Program" not in file and "/AppData/" not in file:
+                        if self.api_scan(file) or self.pe_scan(file) or self.ml_scan(file):
+                            return True
+            elif self.api_scan(value) or self.pe_scan(value) or self.ml_scan(value):
                 return True
             return False
         except:
@@ -1186,17 +1228,16 @@ class MainWindow_Controller(QMainWindow):
                     if p.pid not in existing_processes and file not in self.whitelist:
                         existing_processes.add(p.pid)
                         psutil.Process(p.pid).suspend()
-                        if ":/Windows" in file or ":/Program" in file:
-                            if "powershell" in name and self.api_scan(cmd[-1].split("'")[-2]):
-                                p.kill()
-                                self.send_notify(self.trans("惡意腳本攔截: ")+str(cmd[-1].split("'")[-2]))
-                            elif "cmd.exe" in name and self.api_scan(" ".join(cmd[2:])):
-                                p.kill()
-                                self.send_notify(self.trans("惡意腳本攔截: ")+str(" ".join(cmd[2:])))
-                            elif self.sign_scan(cmd[-1]) and self.api_scan(cmd[-1]):
-                                p.kill()
-                                self.send_notify(self.trans("惡意軟體攔截: ")+str(cmd[-1]))
-                        elif self.api_scan(file) or self.pe_scan(file):
+                        if "powershell" in name and self.scan_proc(cmd[-1].split("'")[-2]):
+                            p.kill()
+                            self.send_notify(self.trans("惡意腳本攔截: ")+str(cmd[-1].split("'")[-2]))
+                        elif "cmd.exe" in name and self.scan_proc(" ".join(cmd[2:])):
+                            p.kill()
+                            self.send_notify(self.trans("惡意腳本攔截: ")+str(" ".join(cmd[2:])))
+                        elif ":/Program" not in file and self.scan_proc(cmd[-1]):
+                            p.kill()
+                            self.send_notify(self.trans("惡意軟體攔截: ")+str(cmd[-1]))
+                        elif ":/Windows" not in file and self.scan_proc(p.pid):
                             p.kill()
                             self.send_notify(self.trans("惡意軟體攔截: ")+file)
                         elif file != self.pyas and self.sign_scan(file):
@@ -1259,8 +1300,9 @@ class MainWindow_Controller(QMainWindow):
         while self.net_protect:
             try:
                 time.sleep(0.5)
+                local_host = socket.gethostbyname(socket.gethostname())
                 for conn in self.proc.connections():
-                    if socket.gethostbyname(socket.gethostname()) == conn.laddr.ip:
+                    if conn.laddr.ip == local_host:
                         self.proc.kill()
                         self.send_notify(self.trans("網路通訊攔截: ")+self.proc.exe().replace("\\", "/"))
             except:

PYAS_Version.py

@@ -1,7 +1,7 @@
 import pyinstaller_versionfile
 pyinstaller_versionfile.create_versionfile(
 output_file="versionfile.txt",
-version='2.9.5',
+version='2.9.6',
 company_name="PYAS Security",
 file_description="Python Antivirus Software",
 internal_name="PYAS",

PYML.py

@@ -0,0 +1,166 @@
+from collections import defaultdict
+import math, json, time, types, random
+
+class ListClassifier:
+    def __init__(self):
+        self.word_weights = defaultdict(lambda: {'ones': 0, 'zero': 0})
+        self.total_counts = {'ones': 0, 'zero': 0}
+        self.prior_probs = {'ones': 0, 'zero': 0}
+        self.class_weights = {'ones': 1, 'zero': 1}
+        self.min_quantity = 0
+        self.max_quantity = 1.0
+        self.learning_rate = 0.1
+        self.max_sequence = 100
+        self.test_data = []
+        self.train_data = []
+
+    def load_model(self, model_data):
+        if isinstance(model_data, str):
+            with open(model_data, 'r') as f:
+                model_data = json.load(f)
+        self.word_weights = defaultdict(lambda: {'ones': 0, 'zero': 0})
+        self.word_weights.update(model_data['word_weights'])
+        self.total_counts = model_data['total_counts']
+        self.prior_probs = model_data['prior_probs']
+        self.max_sequence = model_data.get('max_sequence')
+
+    def save_model(self, filename):
+        model_data = {
+            'word_weights': dict(self.word_weights),
+            'total_counts': self.total_counts,
+            'prior_probs': self.prior_probs,
+            'max_sequence': self.max_sequence}
+        with open(filename, 'w') as f:
+            json.dump(model_data, f)
+        print(f"\nModel Is Safe In {filename}")
+
+    def train_model(self, ones_data, zero_data, epochs=1, batch_size=32, min_quantity=0, max_quantity=1.0, max_sequence=10, test_size=0.2, learning_rate=None, random_seed=None, class_weights=None):
+        self.min_quantity = min_quantity
+        self.max_quantity = max_quantity
+        self.max_sequence = max_sequence
+        self.learning_rate = learning_rate if learning_rate is not None else self.learning_rate
+        self.class_weights = class_weights if class_weights is not None else self.class_weights
+        is_generator = isinstance(ones_data[0], types.GeneratorType)
+        all_data = ones_data + zero_data
+        random.seed(random_seed)
+        random.shuffle(all_data)
+        split_index = int((1 - test_size) * len(all_data))
+        self.train_data = all_data[:split_index]
+        self.test_data = all_data[split_index:]
+        train_accuracies, train_losses = [], []
+        test_accuracies, test_losses = [], []
+        for epoch in range(epochs):
+            print(f'Epoch: {epoch + 1}/{epochs}')
+            train_accuracy, train_loss = self._train_epoch(self.train_data, batch_size, is_generator, prefix='Training:')
+            test_accuracy, test_loss = self._test_epoch(self.test_data, batch_size, is_generator, prefix='Testing: ')
+            train_accuracies.append(train_accuracy)
+            train_losses.append(train_loss)
+            test_accuracies.append(test_accuracy)
+            test_losses.append(test_loss)
+        self._print_table(train_accuracies, train_losses, test_accuracies, test_losses)
+        total_lists = len(self.train_data) + len(self.test_data)
+        self.prior_probs['ones'] = len(ones_data) / total_lists
+        self.prior_probs['zero'] = len(zero_data) / total_lists
+        self._filter_word_weights()
+
+    def _train_epoch(self, data, batch_size, is_generator, prefix=''):
+        start_time = time.time()
+        correct_predictions, total_samples, total_loss = 0, 0, 0
+        for i in range(0, len(data), batch_size):
+            batch = data[i:i + batch_size]
+            for lst_gen in batch:
+                lst = list(lst_gen) if is_generator else lst_gen
+                correct_predictions += self._update_weights(lst, 'ones')
+                total_samples += len(lst)
+                total_loss += self._calculate_loss(lst, 'ones')
+            elapsed_time = time.time() - start_time
+            suffix = f'(Accuracy: {correct_predictions/total_samples:.2%}, Loss: {total_loss/total_samples:.4f}, Elapsed: {elapsed_time:.2f}s)'
+            self._print_progress_bar(i + batch_size, len(data), prefix=prefix, suffix=suffix)
+        print('')
+        accuracy = correct_predictions / total_samples
+        average_loss = total_loss / total_samples
+        return accuracy, average_loss
+
+    def _test_epoch(self, data, batch_size, is_generator, prefix=''):
+        start_time = time.time()
+        correct_predictions, total_samples, total_loss = 0, 0, 0
+        for i in range(0, len(data), batch_size):
+            batch = data[i:i + batch_size]
+            for lst_gen in batch:
+                lst = list(lst_gen) if is_generator else lst_gen
+                correct_predictions += (len(lst) - self._update_weights(lst, 'zero'))
+                total_samples += len(lst)
+                total_loss += self._calculate_loss(lst, 'zero')
+            elapsed_time = time.time() - start_time
+            suffix = f'(Accuracy: {correct_predictions/total_samples:.2%}, Loss: {total_loss/total_samples:.4f}, Elapsed: {elapsed_time:.2f}s)'
+            self._print_progress_bar(i + batch_size, len(data), prefix=prefix, suffix=suffix)
+        print('')
+        accuracy = correct_predictions / total_samples
+        average_loss = total_loss / total_samples
+        return accuracy, average_loss
+
+    def _print_table(self, train_accuracies, train_losses, test_accuracies, test_losses):
+        print("\nEpoch\tTrain Accuracy\tTrain Loss\tTest Accuracy\tTest Loss (Table):")
+        for i, (train_acc, train_loss, test_acc, test_loss) in enumerate(zip(train_accuracies, train_losses, test_accuracies, test_losses), 1):
+            print(f"{i}\t{train_acc:.2%}\t\t{train_loss:.4f}\t\t{test_acc:.2%}\t\t{test_loss:.4f}")
+
+    def _print_progress_bar(self, iteration, total, prefix='', suffix='', length=30, fill='█'):
+        percent = min(100.0, max(0.0, 100 * (iteration / float(total))))
+        percent_string = "{0:.2f}".format(percent)
+        filled_length = int(length * iteration // total)
+        bar = fill * filled_length + ' ' * (length - filled_length)
+        print(f'\r    {prefix} |{bar}| {percent_string}% {suffix}', end='\r')
+
+    def _filter_word_weights(self):
+        for word, weights in list(self.word_weights.items()):
+            total_weight = weights['ones'] + weights['zero']
+            if (total_weight < self.min_quantity or total_weight > self.max_quantity * max(self.total_counts.values())):
+                del self.word_weights[word]
+
+    def _get_data_for_word(self, word, category):
+        data = []
+        if isinstance(self.word_weights[word][category], list):
+            for lst in self.word_weights[word][category]:
+                data.append(lst)
+        return data
+
+    def predict(self, new_list):
+        ones_log_prob = self._calculate_category_log_prob(new_list, 'ones')
+        zero_log_prob = self._calculate_category_log_prob(new_list, 'zero')
+        if ones_log_prob > zero_log_prob:
+            return 1
+        else:
+            return 0
+
+    def _update_weights(self, lst, category):
+        correct_prediction = 0
+        for i, word in enumerate(lst):
+            position_encoding = min(i / len(lst), 1.0)
+            word_frequency = self.word_weights[word][category]
+            learning_rate = self.learning_rate / (word_frequency + 1)
+            self.word_weights[word][category] += learning_rate * self.class_weights[category] * position_encoding
+            self.total_counts[category] += learning_rate
+            prediction = self.predict([word])
+            correct_prediction += 1 if prediction == int(category == 'ones') else 0
+        return correct_prediction
+
+    def _calculate_loss(self, lst, category):
+        loss = 0.0
+        for word in lst:
+            if word in self.word_weights:
+                word_weight_category = self.word_weights[word][category]
+                total_weight_category = self.total_counts[category]
+                prob = (word_weight_category + 1) / (total_weight_category + len(self.word_weights))
+                loss += -math.log(prob)
+        return loss
+
+    def _calculate_category_log_prob(self, lst, category):
+        log_prob = 0.0
+        for word in lst:
+            if word in self.word_weights:
+                word_weight_category = self.word_weights[word][category]
+                total_weight_category = self.total_counts[category]
+                prob = (word_weight_category + 1) / (total_weight_category + len(self.word_weights))
+                log_prob += math.log(prob)
+        log_prob += math.log(self.prior_probs[category] + 1e-10)
+        return log_prob