Create cybersecurity_system.py

cybersecurity_system/cybersecurity_system.py

@@ -0,0 +1,61 @@
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+
+class CybersecuritySystem:
+    def __init__(self, num_features):
+        self.num_features = num_features
+
+    def create_machine_learning_model(self):
+        """
+        Create a machine learning model using Scikit-learn.
+
+        Returns:
+            RandomForestClassifier: The created machine learning model.
+        """
+        model = RandomForestClassifier(n_estimators=100, random_state=42)
+        return model
+
+    def train_machine_learning_model(self, model, X_train, y_train):
+        """
+        Train a machine learning model using Scikit-learn.
+
+        Args:
+            model (RandomForestClassifier): The machine learning model to train.
+            X_train (numpy array): The training input data.
+            y_train (numpy array): The training output data.
+        """
+        model.fit(X_train, y_train)
+
+    def use_machine_learning_model(self, model, input_data):
+        """
+        Use a trained machine learning model to make predictions.
+
+        Args:
+            model (RandomForestClassifier): The trained machine learning model.
+            input_data (numpy array): The input data to make predictions on.
+
+        Returns:
+            numpy array: The predicted output data.
+        """
+        predictions = model.predict(input_data)
+        return predictions
+
+    def optimize_machine_learning_model(self, model, X_train, y_train):
+        """
+        Optimize a machine learning model using a genetic algorithm.
+
+        Args:
+            model (RandomForestClassifier): The machine learning model to optimize.
+            X_train (numpy array): The training input data.
+            y_train (numpy array): The training output data.
+        """
+        from scipy.optimize import differential_evolution
+        def objective_function(params):
+            model.set_params(**params)
+            score = model.score(X_train, y_train)
+            return -score
+        bounds = [(1, 100) for _ in range(5)]  # n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features
+        result = differential_evolution(objective_function, bounds)
+        optimized_params = result.x
+        model.set_params(**optimized_params)
+        return model