恶意样本智能识别怎么创建

import os
import shutil

def collect_samples(malicious_dir, benign_dir):
    # 假设malicious_dir和benign_dir已经包含了相应的样本文件
    malicious_samples = os.listdir(malicious_dir)
    benign_samples = os.listdir(benign_dir)
    
    return malicious_samples, benign_samples

import pefile

def extract_features(file_path):
    pe = pefile.PE(file_path)
    api_calls = [entry.dll for entry in pe.DIRECTORY_ENTRY_IMPORT]
    return api_calls

import pandas as pd

def preprocess_data(malicious_samples, benign_samples):
    data = []
    labels = []
    
    for sample in malicious_samples:
        features = extract_features(os.path.join(malicious_dir, sample))
        data.append(features)
        labels.append(1)  # 恶意样本标记为1
    
    for sample in benign_samples:
        features = extract_features(os.path.join(benign_dir, sample))
        data.append(features)
        labels.append(0)  # 正常样本标记为0
    
    return pd.DataFrame(data), pd.Series(labels)

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

def train_model(data, labels):
    X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
    
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    
    predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    
    return model, accuracy

def evaluate_model(model, data, labels):
    predictions = model.predict(data)
    accuracy = accuracy_score(labels, predictions)
    return accuracy