比賽地址http://bdc.saikr.com/c/cql/34541

賽題

鼠標軌跡識別當前廣泛運用于多種人機驗證產品中，不僅便于用戶的理解記憶，而且極大增加了暴力破解難度。但攻擊者可通過黑產工具產生類人軌跡批量操作以繞過檢測，并在對抗過程中不斷升級其偽造數據以持續繞過同樣升級的檢測技術。我們期望用機器學習算法來提高人機驗證中各種機器行為的檢出率，其中包括對抗過程中出現的新的攻擊手段的檢測。

數據格式

評測指標

F = 5PR/(2P+3R)*100

數據讀取和處理

######數據讀取和處理 import pandas as pd import osdef get_data(file):data1=[]count=0with open(file) as f:for i in f.readlines():count+=1arr = i.split(" ")[1].split(';')[:-1]for j in arr:temp = [count]temp.extend(j.split(','))data1.append(temp)data2=[]with open(file) as f:for i in f.readlines():count += 1arr = i.split(" ")[2]data2.append(arr.split(','))data=pd.DataFrame(data1, columns=["id", 'x', "y", "t"])d2=pd.DataFrame(data2, columns=["target_x", "target_y"])d2.target_y=d2.target_y.apply(lambda x:x[:-1])d2['id'] = range(1, 100001)data = pd.merge(data, d2, on="id")return data

數據可視化

import matplotlib.pyplot as plt %matplotlib inline # plt.xticks(list(range(len(b))), b['x'].values) import os path='F:\\competition_data\\Bigdata\\images' # os.mkdir(path) for i in range(1, 3001):b = data[data.id==i]k = list(b['x'].values)# k.extend(set(b['target_x'].values))l = list(b['y'].values)# l.extend(set(b['target_y'].values))plt.plot(k,l,'o-')fig = plt.gcf()fig.set_size_inches(30,15)fig.savefig(path+'\\'+str(i)+'.png', dpi=100)plt.close()

特征提取

###特征提取 def get_features(data):a=pd.DataFrame()data_length = len(set(data.id.values))import numpy as npfor i in range(data_length):test = data[data.id==i]if len(test) != 1:test.index = range(len(test))temp = test[['x', 'y', 't']].diff(1).dropna()temp['distance'] = np.sqrt(temp['x']**2+temp['y']**2)temp['speed'] = np.log1p(temp['distance']) - np.log1p(temp['t'])temp['angles'] = np.log1p(temp['y'])-np.log1p(temp['x'])speed_diff = temp['speed'].diff(1).dropna()angle_diff = temp['angles'].diff(1).dropna()test['distance_aim_deltas']=np.sqrt((test['x']-test['target_x'])**2+(test['y']-test['target_y'])**2)distance_aim_deltas_diff=test['distance_aim_deltas'].diff(1).dropna()arr=pd.DataFrame(index=[0])arr['id']=iarr['speed_diff_median'] = speed_diff.median()arr['speed_diff_mean'] = speed_diff.mean()arr['speed_diff_var'] = speed_diff.var()arr['speed_diff_max'] = speed_diff.max()arr['angle_diff_var'] = angle_diff.var()arr['time_delta_min'] = temp['t'].min()arr['time_delta_max'] = temp['t'].max()arr['time_delta_var'] = temp['t'].var()arr['distance_deltas_max'] = temp['distance'].max()arr['distance_deltas_var'] = temp['distance'].var()arr['aim_distance_last'] = test['distance_aim_deltas'].values[-1]arr['aim_distance_diff_max'] = distance_aim_deltas_diff.max()arr['aim_distance_diff_var'] = distance_aim_deltas_diff.var()arr['mean_speed'] = temp['speed'].mean()arr['median_speed'] = temp['speed'].median()arr['var_speed'] = temp['speed'].var()arr['max_angle'] = temp['angles'].max()arr['var_angle'] = temp['angles'].var()arr['kurt_angle'] = temp['angles'].kurt()arr['y_min'] = test["y"].min()arr['y_max'] = test["y"].max()arr['y_var'] = test["y"].var()arr['y_mean'] = test["y"].mean()arr['x_min'] = test["x"].min()arr['x_max'] = test["x"].max()arr['x_var'] = test["x"].var()arr['x_mean'] = test["x"].mean()arr['x_back_num'] = min( (test['x'].diff(1).dropna() > 0).sum(), (test['x'].diff(1).dropna() < 0).sum())arr['y_back_num'] = min( (test['y'].diff(1).dropna() > 0).sum(), (test['y'].diff(1).dropna() < 0).sum())arr['xs_delta_var'] = test['x'].diff(1).dropna().var()arr['xs_delta_max'] = test['x'].diff(1).dropna().max()arr['xs_delta_min'] = test['x'].diff(1).dropna().in()# arr['label'] = test['label']a = pd.concat([a,arr])return a

模型

###xgb import xgboost as xgb test_x = test.drop('id', 1) train_x = train.drop(['id', 'label'], 1)dtest = xgb.DMatrix(test_x) # dval = xgb.DMatrix(val_x, label=val_data.label) dtrain = xgb.DMatrix(train_x, label=train.label) params = {'booster': 'gbtree','objective': 'binary:logistic',# 'scale_pos_weight': 1500.0/13458.0,'eval_metric': 'auc','gamma': 0.1, #0.2 is ok'max_depth': 3, # 'lambda': 550,'subsample': 0.7,'colsample_bytree': 0.4, # 'min_child_weight': 2.5,'eta': 0.007 # 'learning_rate': 0.01,'seed': 1024,'nthread': 7, }watchlist = [(dtrain, 'train'), # (dval, 'val')] # The early stopping is based on last set in the evallist model = xgb.train(params,dtrain,feval=feval, # maximize=False,num_boost_round=1500, # early_stopping_rounds=10, # verbose_eval =30,evals=watchlist) # model=xgb.XGBClassifier( # max_depth=4, # learning_rate=0.007, # n_estimators=1500, # silent=True, # objective='binary:logistic', # # booster='gbtree', # # n_jobs=-1, # nthread=7, # # gamma=0, # # min_child_weight=1, # # max_delta_step=0, # subsample=0.7, # colsample_bytree=0.7, # # colsample_bylevel=0.7, # # reg_alpha=0, # # reg_lambda=1, # scale_pos_weight=1, # base_score=0.5, # # random_state=0, # seed=1024, # missing=None, # )# xgb.cv(params,dtrain,num_boost_round=1500,nfold=10,feval=feval,early_stopping_rounds=50,) # model.save_model('./model/xgb.model') # print "best best_ntree_limit",model.best_ntree_limit

評價函數

def eval(clf, x,y):prob = clf.predict(x)for i in range(len(prob)):if prob[i] >= 1:prob[i] = 1else:prob[i] = 0p = ((y==0)&(prob==0)).sum()/(prob==0).sum()print("TP"+" : "+str(((y==0)&(prob==0)).sum()) + " " +"預測"+" :"+str((prob==0).sum())+" " +"真實"+" :"+str((y==0).sum()))r = ((y==0)&(prob==0)).sum()/(y==0).sum()if p==0 or r==0:print(0.0)return 0.0f = 5*p*r/(2*p+3*r)*100print(f)return f def feval(pred, dtrain):y = dtrain.get_label()for i in range(len(pref)):if pred[i] >= 0.5:pred[i] = 1else:pred[i] = 0p = ((y==0) &(pred==0)).sum()/(pred==0).sum()print("-------------------------------------") # print("TP"+" : "+str(((y==0)&(pred==0)).sum())+" "+"預測"+" : "+str((pred==0).sum())+" "+"真實"+" : "+str((y==0).sum()))r = ((y==0)&(pred==0)).sum()/(y==0).sum()if p==0 or r==0:print(0.0)return "f", 0.0f = 5*p*r/(2*p+3*r)*100print(f)return "f", f def target(score, num):x=score*(40000+3*num)/5return x

線下cv

from sklearn import cross_validation score=cross_validation.cross_val_score(m,train.ix[:,1:-1],train.label,cv=10,scoring=eval) score.mean()

提交結果

pred=model.predict(dtest) test['prob']=pred submit=test.sort_values(by="prob").head(20000) submit=submit[['id']] submit=submit.astype(int)

線上成績0.91

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