我正在构建一个分析工具，目前我可以从用户代理获取用户的IP地址、浏览器和操作系统.
我想知道是否有可能在不使用cookie或本地存储的情况下检测到同一个用户？我不希望这里有代码示例；这只是一个简单的提示，说明在哪里可以更进一步.
忘了提一下，如果是同一台计算机/设备，它需要跨浏览器兼容.基本上，我追求的是设备识别，而不是真正的用户.
推荐答案
Introduction
如果我理解正确的话，您需要识别一个没有唯一标识符的用户，所以您需要通过匹配随机数据来找出他们是谁.您无法可靠地存储用户身份，因为:
- Cookies可以被删除
- IP地址可以更改
- 浏览器可以更改
- 浏览器缓存可能会被删除
Java小程序或Com对象本来是一个使用硬件信息散列的简单解决方案，但现在人们非常安全，很难让人们在系统上安装此类程序.这让你不得不使用cookie和其他类似的工具.
Cookies and other, similar tools
您可能会考虑建立数据配置文件，然后使用概率测试来标识Probable User.可通过以下组合生成对此有用的配置文件:
- IP地址
- 曲奇饼
- HTTP Cookie
- 会话Cookie
- 第三方Cookie
- 闪存Cookie(most people don't know how to delete these)
- Web Bugs(可靠性较低，因为Bug已修复，但仍然有用)
- PDF错误
- 闪存错误
- Java错误
- Browsers
- 单击跟踪(许多用户每次访问同一系列页面)
- 浏览器 fingerprint
- 缓存图像(人们有时删除cookie，但保留缓存图像)
- 使用水滴
- URL(浏览器历史记录或cookie可能在URL中包含唯一的用户id，例如https://stackoverflow.com/users/1226894或http://www.facebook.com/barackobama?fref=ts)
- System Fonts Detection(这是一个鲜为人知但通常是唯一的密钥签名)
- HTML5&；Javascript
当然，我列出的项目只是识别用户的几种可能方式.还有很多.
With this set of Random Data elements to build a Data Profile from, what's next?个
下一步是开发大约Fuzzy Logic个，或者更好的是Artificial Neural Network个(使用模糊逻辑).无论是哪种情况，我们的 idea 都是训练您的系统，然后将其训练与Bayesian Inference相结合，以提高结果的准确性.
PHP的NeuralMesh库允许您生成人工神经网络.要实现贝叶斯推理，请查看以下链接:
- Implement Bayesian inference using PHP, Part 1个
- Implement Bayesian inference using PHP, Part 2
- Implement Bayesian inference using PHP, Part 3个
此时，你可能会想:
Why so much Math and Logic for a seemingly simple task?
基本上，因为它是not a simple task.事实上，你想要达到的目标是Pure Probability.例如，给定以下已知用户:
User1 = A + B + C + D + G + K
User2 = C + D + I + J + K + F
当您收到以下数据时:
B + C + E + G + F + K
您实际上在问的问题是:
接收到的数据(B+C+E+G+F+K)实际上是用户1或用户2的概率是多少？这两个匹配中哪一个是most概率？
为了有效地回答这个问题，你需要理解Frequency vs Probability Format以及为什么Joint Probability可能是更好的方法.这里的细节太多了(这就是为什么我给你链接的原因)，但一个很好的例子是Medical Diagnosis Wizard Application，它使用症状的组合来识别可能的疾病.
请考虑一下一系列数据点，它们构成了您的数据配置文件(在上面的示例中，B+C+E+G+F+K)为Symptoms，未知用户为Diseases.通过识别疾病，您可以进一步确定适当的治疗方法(将该用户视为用户1).
显然，我们已经确定了超过1Symptom个的Disease更容易确定.事实上，我们能识别的Symptoms越多，我们的诊断就越容易、越准确，几乎可以肯定.
Are there any other alternatives?个
当然作为一种替代措施，您可以创建自己的简单评分算法，并基于精确匹配.这并不像概率那么有效，但对您来说实施起来可能更简单.
作为一个例子，考虑这个简单的得分图表:
+-------------------------+--------+------------+ | Property | Weight | Importance | +-------------------------+--------+------------+ | Real IP address | 60 | 5 | | Used proxy IP address | 40 | 4 | | HTTP Cookies | 80 | 8 | | 会话Cookie | 80 | 6 | | 第三方Cookies | 60 | 4 | | Flash Cookies | 90 | 7 | | PDF错误 | 20 | 1 | | 闪光虫 | 20 | 1 | | Java错误 | 20 | 1 | | Frequent Pages | 40 | 1 | | Browsers Finger Print | 35 | 2 | | Installed Plugins | 25 | 1 | | Cached Images | 40 | 3 | | URL | 60 | 4 | | System Fonts Detection | 70 | 4 | | Localstorage | 90 | 8 | | Geolocation | 70 | 6 | | AOLTR | 70 | 4 | | 网络信息API | 40 | 3 | | 电池状态API | 20 | 1 | +-------------------------+--------+------------+
对于您可以根据给定请求收集的每一条信息，奖励相关分数，然后在分数相同时使用Importance解决冲突.
Proof of Concept
对于一个简单的概念证明，请看一下Perceptron.感知器是通常用于模式识别应用的RNA Model.甚至有一个旧的PHP Class很好地实现了它，但是您可能需要根据您的目的对其进行修改.
尽管Perceptron是一个很棒的工具，但它仍然可以返回多个结果(可能的匹配)，因此使用分数和差异比较仍然有助于识别这些匹配中的best个.
Assumptions
- 存储每个用户的所有可能信息(IP、cookies等)
- 如果结果完全匹配，则将分数增加1
- 如果结果不完全匹配，则将分数降低1
Expectation
- 生成RNA标签
- 生成模拟数据库的随机用户
- 生成一个未知用户
- 生成未知用户RNA和值
- 该系统将合并RNA信息并教授感知器
- 训练感知机后，系统将有一组权重
- 现在可以测试未知用户的模式，感知器将生成一个结果集.
- 存储所有阳性匹配项
- 首先按分数对匹配项排序，然后按差异排序(如上所述)
- 输出两个最接近的匹配项，如果未找到匹配项，则输出空结果
Code for Proof of Concept
$features = array(
'Real IP address' => .5,
'Used proxy IP address' => .4,
'HTTP Cookies' => .9,
'会话Cookie' => .6,
'第三方Cookies' => .6,
'Flash Cookies' => .7,
'PDF错误' => .2,
'闪光虫' => .2,
'Java错误' => .2,
'Frequent Pages' => .3,
'Browsers Finger Print' => .3,
'Installed Plugins' => .2,
'URL' => .5,
'Cached PNG' => .4,
'System Fonts Detection' => .6,
'Localstorage' => .8,
'Geolocation' => .6,
'AOLTR' => .4,
'网络信息API' => .3,
'电池状态API' => .2
);
// Get RNA Lables
$labels = array();
$n = 1;
foreach ($features as $k => $v) {
$labels[$k] = "x" . $n;
$n ++;
}
// Create Users
$users = array();
for($i = 0, $name = "A"; $i < 5; $i ++, $name ++) {
$users[] = new Profile($name, $features);
}
// Generate Unknown User
$unknown = new Profile("Unknown", $features);
// Generate Unknown RNA
$unknownRNA = array(
0 => array("o" => 1),
1 => array("o" => - 1)
);
// Create RNA Values
foreach ($unknown->data as $item => $point) {
$unknownRNA[0][$labels[$item]] = $point;
$unknownRNA[1][$labels[$item]] = (- 1 * $point);
}
// Start Perception Class
$perceptron = new Perceptron();
// Train Results
$trainResult = $perceptron->train($unknownRNA, 1, 1);
// Find matches
foreach ($users as $name => &$profile) {
// Use shorter labels
$data = array_combine($labels, $profile->data);
if ($perceptron->testCase($data, $trainResult) == true) {
$score = $diff = 0;
// Determing the score and diffrennce
foreach ($unknown->data as $item => $found) {
if ($unknown->data[$item] === $profile->data[$item]) {
if ($profile->data[$item] > 0) {
$score += $features[$item];
} else {
$diff += $features[$item];
}
}
}
// Ser score and diff
$profile->setScore($score, $diff);
$matchs[] = $profile;
}
}
// Sort bases on score and Output
if (count($matchs) > 1) {
usort($matchs, function ($a, $b) {
// If score is the same use diffrence
if ($a->score == $b->score) {
// Lower the diffrence the better
return $a->diff == $b->diff ? 0 : ($a->diff > $b->diff ? 1 : - 1);
}
// The higher the score the better
return $a->score > $b->score ? - 1 : 1;
});
echo "<br />Possible Match ", implode(",", array_slice(array_map(function ($v) {
return sprintf(" %s (%0.4f|%0.4f) ", $v->name, $v->score,$v->diff);
}, $matchs), 0, 2));
} else {
echo "<br />No match Found ";
}
Possible Match D (0.7416|0.16853),C (0.5393|0.2809)
Print_r of "D":
echo "<pre>";
print_r($matchs[0]);
Profile Object(
[name] => D
[data] => Array (
[Real IP address] => -1
[Used proxy IP address] => -1
[HTTP Cookies] => 1
[会话Cookie] => 1
[第三方Cookies] => 1
[Flash Cookies] => 1
[PDF错误] => 1
[闪光虫] => 1
[Java错误] => -1
[Frequent Pages] => 1
[Browsers Finger Print] => -1
[Installed Plugins] => 1
[URL] => -1
[Cached PNG] => 1
[System Fonts Detection] => 1
[Localstorage] => -1
[Geolocation] => -1
[AOLTR] => 1
[网络信息API] => -1
[电池状态API] => -1
)
[score] => 0.74157303370787
[diff] => 0.1685393258427
[base] => 8.9
)
如果Debug=true，您将能够看到Input (Sensor & Desired), Initial Weights, Output (Sensor, Sum, Network), Error, Correction and Final Weights.
+----+----+----+----+----+----+----+----+----+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+------+-----+----+---------+---------+---------+---------+---------+---------+---------+---------+---------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----+----+----+----+----+----+----+----+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----------+
| o | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | x15 | x16 | x17 | x18 | x19 | x20 | Bias | Yin | Y | deltaW1 | deltaW2 | deltaW3 | deltaW4 | deltaW5 | deltaW6 | deltaW7 | deltaW8 | deltaW9 | deltaW10 | deltaW11 | deltaW12 | deltaW13 | deltaW14 | deltaW15 | deltaW16 | deltaW17 | deltaW18 | deltaW19 | deltaW20 | W1 | W2 | W3 | W4 | W5 | W6 | W7 | W8 | W9 | W10 | W11 | W12 | W13 | W14 | W15 | W16 | W17 | W18 | W19 | W20 | deltaBias |
+----+----+----+----+----+----+----+----+----+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+------+-----+----+---------+---------+---------+---------+---------+---------+---------+---------+---------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----+----+----+----+----+----+----+----+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----------+
| 1 | 1 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 0 | -1 | 0 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | 1 | 1 | 0 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 |
| -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | -1 | -1 | 1 | -19 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 |
| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
| 1 | 1 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 19 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 |
| -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | -1 | -1 | 1 | -19 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | -1 | -1 | 1 | 1 | 1 |
| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
+----+----+----+----+----+----+----+----+----+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+------+-----+----+---------+---------+---------+---------+---------+---------+---------+---------+---------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----+----+----+----+----+----+----+----+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----------+
x1到x20表示代码转换的特征.
// Get RNA Labels
$labels = array();
$n = 1;
foreach ( $features as $k => $v ) {
$labels[$k] = "x" . $n;
$n ++;
}
这是online demo美元
Class Used:
class Profile {
public $name, $data = array(), $score, $diff, $base;
function __construct($name, array $importance) {
$values = array(-1, 1); // Perception values
$this->name = $name;
foreach ($importance as $item => $point) {
// Generate Random true/false for real Items
$this->data[$item] = $values[mt_rand(0, 1)];
}
$this->base = array_sum($importance);
}
public function setScore($score, $diff) {
$this->score = $score / $this->base;
$this->diff = $diff / $this->base;
}
}
Modified Perceptron Class
class Perceptron {
private $w = array();
private $dw = array();
public $debug = false;
private function initialize($colums) {
// Initialize perceptron vars
for($i = 1; $i <= $colums; $i ++) {
// weighting vars
$this->w[$i] = 0;
$this->dw[$i] = 0;
}
}
function train($input, $alpha, $teta) {
$colums = count($input[0]) - 1;
$weightCache = array_fill(1, $colums, 0);
$checkpoints = array();
$keepTrainning = true;
// Initialize RNA vars
$this->initialize(count($input[0]) - 1);
$just_started = true;
$totalRun = 0;
$yin = 0;
// Trains RNA until it gets stable
while ($keepTrainning == true) {
// Sweeps each row of the input subject
foreach ($input as $row_counter => $row_data) {
// Finds out the number of columns the input has
$n_columns = count($row_data) - 1;
// Calculates Yin
$yin = 0;
for($i = 1; $i <= $n_columns; $i ++) {
$yin += $row_data["x" . $i] * $weightCache[$i];
}
// Calculates Real Output
$Y = ($yin <= 1) ? - 1 : 1;
// Sweeps columns ...
$checkpoints[$row_counter] = 0;
for($i = 1; $i <= $n_columns; $i ++) {
/** DELTAS **/
// Is it the first row?
if ($just_started == true) {
$this->dw[$i] = $weightCache[$i];
$just_started = false;
// Found desired output?
} elseif ($Y == $row_data["o"]) {
$this->dw[$i] = 0;
// Calculates Delta Ws
} else {
$this->dw[$i] = $row_data["x" . $i] * $row_data["o"];
}
/** WEIGHTS **/
// Calculate Weights
$this->w[$i] = $this->dw[$i] + $weightCache[$i];
$weightCache[$i] = $this->w[$i];
/** CHECK-POINT **/
$checkpoints[$row_counter] += $this->w[$i];
} // END - for
foreach ($this->w as $index => $w_item) {
$debug_w["W" . $index] = $w_item;
$debug_dw["deltaW" . $index] = $this->dw[$index];
}
// Special for script debugging
$debug_vars[] = array_merge($row_data, array(
"Bias" => 1,
"Yin" => $yin,
"Y" => $Y
), $debug_dw, $debug_w, array(
"deltaBias" => 1
));
} // END - foreach
// Special for script debugging
$empty_data_row = array();
for($i = 1; $i <= $n_columns; $i ++) {
$empty_data_row["x" . $i] = "--";
$empty_data_row["W" . $i] = "--";
$empty_data_row["deltaW" . $i] = "--";
}
$debug_vars[] = array_merge($empty_data_row, array(
"o" => "--",
"Bias" => "--",
"Yin" => "--",
"Y" => "--",
"deltaBias" => "--"
));
// Counts training times
$totalRun ++;
// Now checks if the RNA is stable already
$referer_value = end($checkpoints);
// if all rows match the desired output ...
$sum = array_sum($checkpoints);
$n_rows = count($checkpoints);
if ($totalRun > 1 && ($sum / $n_rows) == $referer_value) {
$keepTrainning = false;
}
} // END - while
// Prepares the final result
$result = array();
for($i = 1; $i <= $n_columns; $i ++) {
$result["w" . $i] = $this->w[$i];
}
$this->debug($this->print_html_table($debug_vars));
return $result;
} // END - train
function testCase($input, $results) {
// Sweeps input columns
$result = 0;
$i = 1;
foreach ($input as $column_value) {
// Calculates teste Y
$result += $results["w" . $i] * $column_value;
$i ++;
}
// Checks in each class the test fits
return ($result > 0) ? true : false;
} // END - test_class
// Returns the html code of a html table base on a hash array
function print_html_table($array) {
$html = "";
$inner_html = "";
$table_header_composed = false;
$table_header = array();
// Builds table contents
foreach ($array as $array_item) {
$inner_html .= "<tr>\n";
foreach ( $array_item as $array_col_label => $array_col ) {
$inner_html .= "<td>\n";
$inner_html .= $array_col;
$inner_html .= "</td>\n";
if ($table_header_composed == false) {
$table_header[] = $array_col_label;
}
}
$table_header_composed = true;
$inner_html .= "</tr>\n";
}
// Builds full table
$html = "<table border=1>\n";
$html .= "<tr>\n";
foreach ($table_header as $table_header_item) {
$html .= "<td>\n";
$html .= "<b>" . $table_header_item . "</b>";
$html .= "</td>\n";
}
$html .= "</tr>\n";
$html .= $inner_html . "</table>";
return $html;
} // END - print_html_table
// Debug function
function debug($message) {
if ($this->debug == true) {
echo "<b>DEBUG:</b> $message";
}
} // END - debug
} // END - class
Conclusion
识别没有唯一标识符的用户不是一项直接或简单的任务.这取决于收集足够量的随机数据，您可以通过各种方法从用户那里收集这些数据.
即使你 Select 不使用人工神经网络，我建议至少使用一个简单的概率矩阵，带有优先级和可能性——我希望上面提供的代码和示例能够让你继续下go .
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