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| #import fireducks.pandas as pd | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import LabelEncoder, StandardScaler | |
| class DataProcessor: | |
| def __init__(self, csv_file): | |
| self.data = pd.read_csv(csv_file) | |
| self.label_encoders = {} | |
| self.scaler = StandardScaler() | |
| def preprocess_data(self): | |
| for column in ['IP', 'Hostnames', 'OS']: | |
| self.label_encoders[column] = LabelEncoder() | |
| self.data[column] = self.label_encoders[column].fit_transform(self.data[column].astype(str)) | |
| self.data['Port'] = self.scaler.fit_transform(self.data[['Port']]) | |
| self.data['Timestamp'] = pd.to_datetime(self.data['Timestamp']).astype(int) / 10**9 | |
| return self.data | |
| def get_features_and_labels(self, label_column='Anomaly'): | |
| if label_column not in self.data.columns: | |
| raise ValueError(f"Label column '{label_column}' not found in data.") | |
| X = self.data.drop([label_column], axis=1) | |
| y = self.data[label_column] | |
| return X, y | |
| def split_data(self, X, y, test_size=0.2, random_state=42): | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state) | |
| return X_train, X_test, y_train, y_test | |
| # # Example usage: | |
| # processor = DataProcessor('shodan_scan_results.csv') | |
| # processed_data = processor.preprocess_data() | |
| # X, y = processor.get_features_and_labels() | |
| # X_train, X_test, y_train, y_test = processor.split_data(X, y) | |