The document discusses techniques for detecting fraudulent activities using machine learning, specifically focusing on anomaly detection and supervised classifiers. It includes code snippets demonstrating algorithms for evaluating fraud detection models, analyzing user behavior, and improving prediction accuracy. Various methods such as statistical analysis, sensitivity, and training data evaluation are detailed to enhance classifiers' performance in identifying fraudulent transactions.

1. TEACHING YOUR
MACHINE
TO FIND
FRAUDSTERS
Ian Barber
ianb@php.net
phpir.com
twitter.com/ianbarber

2. http://joind.in/3429
https://github.com/ianbarber/FindingFraudsters-Talk

3. 5%
3%
SOME .1%
SMALL
NUMBERS 8%

4. 99%
ACCURACY

5. REALLY REALLY
LEGITIMATE FRAUD
EVALUATED
989 0
LEGITIMATE
EVALUATED
10 1
FRAUD

6. REALLY REALLY
LEGITIMATE FRAUD
90%
EVALUATED
LEGITIMATE
WR ONG989 0
EVALUATED
10 1
FRAUD

7. ANOMALY DETECTION

8. 30
22.5
Clicks
15
7.5
0
Date

9. SOFTWARE
ARCHITECTURE
Alarm
Detector
No Alarm
Buffer
User Clicks Landing
Ad Page

10. DETECTOR
statistics
Expected
Clicks
Threshold Data Buffer
Sensitivity
Alarm

11. average.php
function detect($sen) {
$window = array(); $i = 0;
$alarmCount = 0; $dtd = 0;
$avg = $stddev = 0;
$fraud = fopen("fraudclicks.csv", 'r');
while($d = fgetcsv($fraud)) {
$i++;
if(count($window) > 7) {
array_shift($window);
$avg = array_sum($window) / 7;
foreach($window as $val) {
$stddev += pow($val - $average, 2);
}
$stddev = sqrt($stddev/7);

12. 0.2
0.15
0.1
0.05
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

13. if($d[1] > ($avg + ($sen * $stddev))){
$alarmCount++;
if($i > 201) {
break;
}
} else {
if($i > 201) {
$dtd++;
}
}
}
array_push($window, $d[1]);
}
return array($alarmCount-1, $dtd);
}

14. 1.6 SENSITIVITY
30
18 False Alarms 1 Day To Detect
22.5
Clicks
15
7.5
0
Date

15. 2.7 SENSITIVITY
30
1 False Alarm 18 Days To Detect
22.5
Clicks
15
7.5
0
Date

16. SICKNESS
AVAILABILITY

17. function detect($sens) { sickavail.php
$i = 0; $alarms = 0; $dtd = 0;
$window = array(); $avail = array();
$fraud = fopen("fraudclicks.csv", 'r');
while($dat = fgetcsv($fraud)) {
$dow = date("w", strtotime($dat[0]));
if( count($window) >= 7
&& isset($avail[$dow]) ) {
$sick = 0;
foreach($window as $day => $value) {
$dowavg = array_sum($avail[$day]) /
count($avail[$day]);
$sick += $value / $dowavg;
}
$sick /= count($window);

18. $avlblty = array_sum($avail[$dow]) /
count($avail[$dow]);
$est = $sick * $avlblty;
$fac = fac($dat[1]);
$p = exp(-$est) * pow($est,$dat[1])
/ $fac; // poisson calc
if($p < $sens && $dat[1] > $est) {
$alarms++;
if($i > 201) { break; }
} else {
if($i > 201) { $dtd++; }
}
} // end if

19. 0.2
0.15
0.1
0.05
0
1 2 3 4 5 6 7 8 9 10

20. 0.011 SENSITIVITY
30
1 False Alarm 1 Day To Detect
22.5
Clicks
15
7.5
0
Date

21. SUPERVISED CLASSIFIERS

22. classification model
SOFTWARE
ARCHITECTURE
Fraud
Classifier
Not Fraud
User Transaction
Purchase Processor
Transaction
Learner
Database

23. EVALUATING THE CLASSIFIER
Training Data Learner Model
Test Data
Prediction
Classifier Accuracy
Model

24. 20
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27. $docs = array(
array('fraud' => false, 'price' => 1699,
'desc'=>'toy ninja', 'ship' => 'US'),
array('fraud' => false, 'price' => 20000,
'desc' => 'TV','ship' => 'US'),
array('fraud' => false, 'price' => 2500,
'desc' => 'cds', 'ship' => 'US'),
array('fraud' => true, 'price' => 20000,
'desc' => 'console', 'ship' => 'CN'),
array('fraud' => true, 'price' => 5000,
'desc' => 'books', 'ship' => 'US'),
array('fraud' => true, 'price' => 15000,
'desc' => 'ipod', 'ship' => 'CN'),
);

28. $db = new XapianWritableDatabase("index",
Xapian::DB_CREATE_OR_OPEN);
$idx = new XapianTermGenerator();
$stem = new XapianStem("english");
$idx->set_stemmer($stem);
foreach($docs as $key => $doc) {
$xdoc = new XapianDocument();
$xdoc->set_data($doc['fraud'] ?
"fraud" : "clean");
$idx->set_document($xdoc);
$idx->index_text($doc['price'] . ' ' .
$doc['desc'] . ' ' . $doc['ship']);
$db->add_document($xdoc, $key);
}
$db = null;
frau dknn.php

29. $test = array( testknn.ph
p
'price' => 10000, 'desc' => 'TV',
'ship' => 'CN'
);
$db = new XapianWritableDatabase("index",
Xapian::DB_CREATE_OR_OPEN);
$idx = new XapianTermGenerator();
$stem = new XapianStem("english");
$idx->set_stemmer($stem);
$xdoc = new XapianDocument();
$idx->set_document($xdoc);
$idx->index_text($test['price'] . ' ' .
$test['desc'] . ' ' . $test['ship']);
$id = $db->add_document($xdoc);

30. $enq = new XapianEnquire($db);
$rset = new XapianRSet();
$rset->add_document($id);
$eset = $enq->get_eset(10, $rset);
$terms = array();
$i = $eset->begin();
while ( !$i->equals($eset->end()) ) {
$terms[] = $i->get_term(); $i->next();
}
$q = new XapianQuery(
XapianQuery::OP_OR, $terms);
$enq->set_query($q);
$matches = $enq->get_mset(0, 4, $rset);

31. $i = $matches->begin();
while (!$i->equals($matches->end())) {
if($i->get_document()->get_docid() != $id)
{
$class = $i->get_document()->get_data();
var_dump($class);
}
$i->next();
}
$db->delete_document($id);
$ php testknn.php
string(5) "clean"
string(5) "fraud"
string(5) "fraud"

32. TRANSACTION
PARAMETERS

35. function compareEmailToName($name, $email) {
$name = strtolower($name);
$email = strtolower($email);
$parts = explode(" ", $name);
$pcnt = 0;
list($user, $dom) = explode("@", $email);
$user = str_replace(
array(".", "+"), " ", $user);
$dom = preg_replace("/..*/", "", $dom);
similar_text($name, $user, $pcnt);
if($pcnt > 80) { return 1.0; }
similar_text($name, $dom, $pcnt);
if($pcnt > 80) { return 0.8; }
email.php

36. if(count($parts)) {
$highest = 0;
foreach($parts as $part) {
similar_text($user, $part, $pcnt);
if($pcnt > 50 && $pcnt > $highest) {
$highest = $percent;
}
similar_text($dom, $part, $pcnt);
if($pcnt > 50 && $pcnt > $highest) {
$highest = $percent;
}
}
return (1.7 * ($highest/100)) - 1;
}
return -1;
}

40. $data = array(
'purchase_value' => 20993,
'geo_country' => 'DE',
'previous_orders' => 1,
'time' => 6,
'timegap' => 146632,
'product_category' => 'small_item',
'delivery_matches_card' => 0,
'geo_ip_matches_card' => 1,
'difference_from_last_trans' => 8755,
'free_shipping' => 0,
'email_like_name' => 0,
'free_email_provider' => 0,
'disposable_email_provider' => 0,
'quantity' => 2,
'fraud' => 0);

41. SUPPORT
VECTOR MACHINES

42. 20
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43. 20
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0 5 10 15 20

45. 20
15
10
5
0
0 5 10 15 20

46. 20
15
10
5
0
0 5 10 15 20

47. $ apt-get install libsvm-dev
$ apt-get install libsvm-tools
$ yum install libsvm-devel
$ pecl install svm-beta
$ echo extension=svm.so > /etc/php.d/svm.ini
$ php -r '$s = new svm(); $m = $s->train
(array(array(-1, -1), array(1, 1))); echo
$m->predict(array(0, -1));'
-1

48. $fh = fopen('paydata.csv', 'r');
$output = array();
while($data = fgetcsv($fh)) {
$output[] = array(
$data[14] == 1 ? -1 : 1,
1 => ($data[0]/20000.00) - 1.0, // price
2 => $data[1] == 'CN' ? 1.0:-1.0,
3 => $data[1] == 'US' ? 1.0:-1.0,
4 => $data[5] == 'digital' ? 1.0:-1.0,
5 => $data[7] == 1 ? 1.0:-1.0, //geo
6 => $data[6] == 1 ? 1.0:-1.0, // deliv
12 => $data[9] == 1 ? 1.0:-1.0, // ship
13 => ($data[13] / 1.5) - 1.0, // qty
);
} learn.php

49. $svm = new svm();
$model = $svm->train($output,
array(-1 => 0.65, 1 => 0.5));
$model->save('learn.model');
$fp = $tp = $fn = $tn = 0;
foreach($output as $test) {
$res = $model->predict($test);
if($test[0] > 0) {
if($res > 0) { $tp++; }
else { $fn++; }
} else {
if($res > 0) { $fp++; }
else { $tn++; }
}
}

50. // ...snip.. loading test data from
// paytest.csv
$model = new SVMModel('learn.model');
$fp = $tp = $fn = $tn = 0;
foreach($output as $test) {
$res = $model->predict($test);
if($test[0] > 0) {
if($res > 0) { $tp++; }
else { $fn++; }
} else {
if($res > 0) { $fp++; }
else { $tn++; }
}
}
test.php

51. var_dump("True Positive " . $tp);
var_dump("True Negative " . $tn);
var_dump("False Positive " . $fp);
var_dump("False Negative " . $fn);
var_dump("Accuracy " .
(($tp+$tn)/($tp+$tn+$fp+$fn)));

52. $ php learn.php
string(18) "True Positive 8316"
string(18) "True Negative 1682"
string(16) "False Positive 2"
string(16) "False Negative 0"
string(15) "Accuracy 0.9998"
$ php test.php
string(17) "True Positive 844"
string(17) "True Negative 155"
string(16) "False Positive 0"
string(16) "False Negative 1"
string(14) "Accuracy 0.999"

53. training data
Test Verify Update
Automated Manual Manual

54. Time Series Class Based
Sensitivity Model
False Days To False False
Alarms Detect Positives Negatives

55. (shogun)

56. TEACHING YOUR
MACHINE
TO FIND
FRAUDSTERS
http://joind.in/3429
Ian Barber
ianb@php.net

57. Title Slide - CSI
http://www.flickr.com/photos/39matt/5241862082
Sickness Availability - Chicago Fire Department
http://www.flickr.com/photos/mike_miley/3929146730/
Model Buildings - Ah Ain’t Long For This Whorl
http://www.flickr.com/photos/chadmiller/98014022/
Repeat Customer - McDonald’s Loyalty Card
http://www.flickr.com/photos/fsse-info/3658873057/
Shipping - FedEx Truck
http://www.flickr.com/photos/moto_club4ag/4852235145/
Velocity - Chevrolet Chevelle Dragster
http://www.flickr.com/photos/jns001/2958999006/
GeoIP - Earth Asia Terminator View
http://www.flickr.com/photos/flyingsinger/86898564/
Multiple Items - Boxes
http://www.flickr.com/photos/skrewtape/851672959/