第二届高校大数据比赛之鼠标轨迹识别

比赛地址http://bdc.saikr.com/c/cql/34541

赛题

鼠标轨迹识别当前广泛运用于多种人机验证产品中,不仅便于用户的理解记忆,而且极大增加了暴力破解难度。但攻击者可通过黑产工具产生类人轨迹批量操作以绕过检测,并在对抗过程中不断升级其伪造数据以持续绕过同样升级的检测技术。我们期望用机器学习算法来提高人机验证中各种机器行为的检出率,其中包括对抗过程中出现的新的攻击手段的检测。

数据格式

第二届高校大数据比赛之鼠标轨迹识别_第1张图片

评测指标

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

数据读取和处理

######数据读取和处理
import pandas as pd
import os

def get_data(file):
    data1=[]
    count=0
    with open(file) as f:
        for i in f.readlines():
            count+=1
            arr = 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 += 1
            arr = 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

数据可视化

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 np
    for 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']=i
            arr['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  

评价函数

def eval(clf, x,y):
    prob = clf.predict(x)
    for i in range(len(prob)):
        if prob[i] >= 1:
            prob[i] = 1
        else:
            prob[i] = 0
    p = ((y==0)&(prob==0)).sum()/(prob==0).sum()
    print("TP"+" : "+str(((y==0)&(prob==0)).sum()) + " " +"预测"+" :"+str((prob==0).sum())+" " +"真实"+" :"+str((y==0).sum()))
    r = ((y==0)&(prob==0)).sum()/(y==0).sum()
    if p==0 or r==0:
        print(0.0)
        return 0.0

    f = 5*p*r/(2*p+3*r)*100
    print(f)
    return f
def feval(pred, dtrain):
    y = dtrain.get_label()
    for i in range(len(pref)):
        if pred[i] >= 0.5:
            pred[i] = 1
        else:
            pred[i] = 0
    p = ((y==0) &(pred==0)).sum()/(pred==0).sum()
    print("-------------------------------------")
#   print("TP"+" : "+str(((y==0)&(pred==0)).sum())+"  "+"预测"+" : "+str((pred==0).sum())+"  "+"真实"+" : "+str((y==0).sum()))
    r = ((y==0)&(pred==0)).sum()/(y==0).sum()
    if p==0 or r==0:
        print(0.0)
        return "f", 0.0

    f = 5*p*r/(2*p+3*r)*100
    print(f)
    return "f", f
def target(score, num):
    x=score*(40000+3*num)/5
    return x

线下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()

提交结果

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

线上成绩0.91

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