Case 4. MLP for nonlinear phishing risk (RubixML)

Implementation in RubixML

We solve this task with a multilayer neural network. In this case, phishing risk is estimated from two features: employee security-awareness level and current workload. The dependency can be nonlinear, so we use an MLP (Multilayer Perceptron) from RubixML: build a labeled dataset, standardize features with ZScaleStandardizer, train the model, and get a prediction for a new employee profile.

Example of use


                
<?php

use Rubix\ML\Datasets\Unlabeled;

include 'code.php';

$inputSample = [4, 6];
$predictDataset = new Unlabeled([$inputSample]);
$predictDataset->apply($standardizer);
$prediction = $model->predict($predictDataset);

echo 'Phishing risk prediction:' . PHP_EOL;
echo '"' . $prediction[0] . '"' . PHP_EOL;

Documents:


            [1, 6] => risk
[5, 3] => safe
[10, 1] => risk
[3, 7] => safe

Check: [4, 6]

Result: Memory: 1.379 Mb Time running: 0.063 sec.


                Phishing risk prediction:
"safe"