211 lines
7.3 KiB
Python
211 lines
7.3 KiB
Python
import pandas as pd
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from sklearn.model_selection import StratifiedKFold
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from pipeline.ngrams_classif import NgramsExtractor
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from sklearn.pipeline import FeatureUnion, Pipeline
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score
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import numpy as np
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import warnings
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warnings.filterwarnings('ignore')
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def average(arr: list):
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return sum(arr) / len(arr)
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def classify_ndss(train, test):
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# print(test)
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combinedFeatures = FeatureUnion([
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# ('tsfresh', TSFreshBasicExtractor()),
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('ngrams', NgramsExtractor(max_ngram_len=1)),
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])
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pipeline = Pipeline([
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('features', combinedFeatures),
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('clf', RandomForestClassifier(n_estimators=100, max_depth=30, min_samples_leaf=5)),
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# ('clf', RandomForestClassifier(n_estimators=100)),
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])
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pipeline.fit(train, list(train.class_label))
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# Prediction
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pred_ret = pipeline.predict(test)
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return pred_ret
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def classify_ndss_key(train, test):
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# print(test)
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combinedFeatures = FeatureUnion([
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# ('tsfresh', TSFreshBasicExtractor()),
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('ngrams', NgramsExtractor(max_ngram_len=1)),
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])
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pipeline = Pipeline([
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('features', combinedFeatures),
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('clf', RandomForestClassifier(n_estimators=100, max_depth=30, min_samples_leaf=5)),
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# ('clf', RandomForestClassifier(n_estimators=100)),
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])
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pipeline.fit(train, list(train.class_label))
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# Prediction
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pred_ret = pipeline.predict(test)
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return pred_ret
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def classify_rf(train, test):
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rf = RandomForestClassifier()
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X = []
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Y = []
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for index, row in train.iterrows():
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lengths = list(row['lengths'])
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if len(lengths) > 100:
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lengths = lengths[:100]
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elif len(lengths) < 100:
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while len(lengths) < 100:
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lengths.append(0)
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X.append(lengths)
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label = int(row['class_label'])
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Y.append(label)
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rf.fit(X, Y)
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# return
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test_X = []
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for index, row in test.iterrows():
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lengths = list(row['lengths'])
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if len(lengths) > 100:
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lengths = lengths[:100]
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elif len(lengths) < 100:
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while len(lengths) < 100:
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lengths.append(0)
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test_X.append(lengths)
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pred_ret = rf.predict(test_X)
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return pred_ret
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def trans_csv_to_df(csv_filename):
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src_df = pd.read_csv(csv_filename)
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dst_df = pd.DataFrame()
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for i in range(len(src_df)):
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features = np.array(eval(src_df.loc[i, 'lengths']))
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label = src_df.loc[i, 'class_label']
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# print(label,type(label))
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if label >= 100:
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continue
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# dst_df.
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dst_df = dst_df.append({
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"lengths": features,
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"class_label": label
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}, ignore_index=True)
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return dst_df
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def exp(classifier, feature_model, data_model, ops_mode="win10"):
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if classifier == "rf":
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classify = classify_rf
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elif classifier == "ndss":
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if feature_model == "norm":
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classify = classify_ndss
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elif feature_model == "key":
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classify = classify_ndss_key
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else:
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print("feature_model", feature_model)
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return
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else:
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print("未知classifier", classifier)
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return
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if feature_model in ["key", "norm"]:
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df = trans_csv_to_df(f"./data/{feature_model}_feature_{data_model}_{ops_mode}.csv")
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else:
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print("未知特征类别！")
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return
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kf = StratifiedKFold(n_splits=10, shuffle=True)
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precisions = []
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recalls = []
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f1s = []
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accs = []
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for k, (train, test) in enumerate(kf.split(df, list(df.class_label))):
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if classifier == "ndss":
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predict_results = classify(df.iloc[train], df.iloc[test])
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elif classifier == "rf":
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predict_results = classify(df.iloc[train], df.iloc[test])
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else:
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print("未知分类方法")
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return
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gt_Y = df.iloc[test].class_label
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precision = precision_score(gt_Y, predict_results, average='weighted')
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recall = recall_score(gt_Y, predict_results, average='weighted')
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f1 = f1_score(gt_Y, predict_results, average='weighted')
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acc = accuracy_score(gt_Y, predict_results)
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precisions.append(precision)
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recalls.append(recall)
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f1s.append(f1)
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accs.append(acc)
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break
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print("平均准确率: ", average(precisions), end="\t")
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print("平均召回率: ", average(recalls), end="\t")
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print("平均f1值: ", average(f1s), end="\t")
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print("平均acc: ", average(accs))
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def cross_validation(classifier, feature_model):
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if classifier == "rf":
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classify = classify_rf
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elif classifier == "ndss":
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if feature_model == "norm":
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classify = classify_ndss
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elif feature_model == "key":
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classify = classify_ndss_key
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else:
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print("feature_model", feature_model)
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return
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else:
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print("未知classifier", classifier)
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return
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# train = "firefox"
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# test = "chrome"
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print("classifier:", classifier)
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print("feature_model", feature_model)
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# for train, test in [("chrome", "edge"), ("chrome", "firefox"), ("firefox", "chrome"), ("firefox", "edge"),
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# ("edge", "chrome"), ("edge", "firefox")]:
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# print("train:", train, "test:", test)
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# df_train = trans_csv_to_df(f"./data/{feature_model}_feature_{train}_win10.csv")
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# df_test = trans_csv_to_df(f"./data/{feature_model}_feature_{test}_win10.csv")
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# predict_results = classify(df_train, df_test)
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# gt_Y = df_test.class_label
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# precision = precision_score(gt_Y, predict_results, average='weighted')
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# recall = recall_score(gt_Y, predict_results, average='weighted')
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# f1 = f1_score(gt_Y, predict_results, average='weighted')
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# acc = accuracy_score(gt_Y, predict_results)
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# print("准确率: ", precision, end="\t")
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# print("召回率: ", recall, end="\t")
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# print("f1值: ", f1, end="\t")
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# print("acc: ", acc)
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for train, test in [("win10", "ubuntu"), ("ubuntu", "win10")]:
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print("train:", train, "test:", test)
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df_train = trans_csv_to_df(f"./data/{feature_model}_feature_chrome_{train}.csv")
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df_test = trans_csv_to_df(f"./data/{feature_model}_feature_chrome_{test}.csv")
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predict_results = classify(df_train, df_test)
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gt_Y = df_test.class_label
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precision = precision_score(gt_Y, predict_results, average='weighted')
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recall = recall_score(gt_Y, predict_results, average='weighted')
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f1 = f1_score(gt_Y, predict_results, average='weighted')
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acc = accuracy_score(gt_Y, predict_results)
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print("准确率: ", precision, end="\t")
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print("召回率: ", recall, end="\t")
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print("f1值: ", f1, end="\t")
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print("acc: ", acc)
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if __name__ == '__main__':
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exp("ndss", "norm", "firefox")
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exp("ndss", "key", "firefox")
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#
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exp("ndss", "norm", "chrome")
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exp("ndss", "key", "chrome")
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exp("ndss", "norm", "chrome", "ubuntu")
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exp("ndss", "key", "chrome", "ubuntu")
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cross_validation("ndss", "norm")
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cross_validation("ndss", "key")
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