处理d

关系到

D = ?
D == T['sample'](this['traceData'],    V)// 获取this['traceData']的前50个数据

往回翻，反混淆分析鼠标事件函数：

this['traceData']应该就是滑动轨迹，通过鼠标事件读取

var w6 = N(w4, w5 + '');
this['traceData'].push(w6)

而w4和w5又从何得来？

var w4 = this[kV(0x2d0)]['state']['token']
 w5 = [Math[kV(0x17a)](w0[kV(0x466)] < 0x0 ? 0x0 : w0[kV(0x466)]), Math[kV(0x17a)](w0['clientY'] - w0['startY']), T[kV(0x436)]() - w0[kV(0x3da)]];

观察w4,发现是token，解决

观察w5

阅读整个分析代码，得知clientX,clientY,dragX,是坐标轨迹计算算法。

然后得知w5的意义：

w5 = [移动的横坐标，移动的纵坐标，当前时间-开始时间]

w6 = N(w4, w5 + '');

接下来是算N了，可以用嫁接法：

N函数解决后，就可以模拟实现this['traceData']

整合代码：

import subprocess
import random
def N_w8(v1, v2):
    res = subprocess.check_output(f"node N1.js {v1} {v2}")
    res = res.decode('utf-8').strip()
    return res
def run():
    # 识别滑块的距离
    x_distance = 100
    # 背景请求返回的token
    bg_token = "cc7c74c959d842e69aa980a3430b7884"
    trace_data = []
    interval_value = random.randint(100, 300)
    step = 2
    for i in range(0, x_distance + 1, step):
        x_value = i + step
        if x_value > x_distance:
            x_value = x_distance
        interval_value += random.randint(10, 20)
        y_value = random.randint(0, 5)
        # trace_data
        line = f"{x_value},{y_value},{interval_value}"
        line = N_w8(bg_token, line)
        trace_data.append(line)
    print(trace_data)
if __name__ == '__main__':
    run()

距离D的辅助，还差搞定J函数，发现J函数到处用。

直接上嫁接法：

最终整合

import subprocess
import random
def N_w8(v1, v2):
    res = subprocess.check_output(f"node N1.js {v1} {v2}")
    res = res.decode('utf-8').strip()
    return res
def j_as_ww(v1):
    res = subprocess.check_output(f"node j.js {v1}")
    res = res.decode('utf-8').strip()
    return res
def run():
    # 识别滑块的距离
    x_distance = 100
    # 背景请求返回的token
    bg_token = "cc7c74c959d842e69aa980a3430b7884"
    trace_data = []
    interval_value = random.randint(100, 300)
    step = 2
    for i in range(0, x_distance + 1, step):
        x_value = i + step
        if x_value > x_distance:
            x_value = x_distance
        interval_value += random.randint(10, 20)
        y_value = random.randint(0, 5)
        # trace_data
        line = f"{x_value},{y_value},{interval_value}"
        line = N_w8(bg_token, line)
        trace_data.append(line)
    d_string = j_as_ww(":".join(trace_data[0:50]))
    print(d_string)
if __name__ == '__main__':
    run()