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<!DOCTYPE html><html lang="en-US"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,height=device-height,initial-scale=1.0"><meta name="apple-mobile-web-app-capable" content="yes"><meta http-equiv="X-UA-Compatible" content="ie=edge"><meta property="og:type" content="website"><meta name="twitter:card" content="summary"><style>@media screen{body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button{-webkit-tap-highlight-color:transparent;-webkit-appearance:none;appearance:none;background-color:transparent;border:0;color:inherit;cursor:pointer;font-size:inherit;opacity:.8;outline:none;padding:0;transition:opacity .2s linear}body[data-bespoke-view=""] 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*
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<h1>Network Intrusion Detection</h1>
<p>by Charaf-Eddine M'rah</p>
</section>
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<h2>1- Introduction</h2>
</section>
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<h3>Objective of the Project</h3>
<p>The main objective of this project is to build a machine learning model that can detect network intrusions and other malicious activity with high accuracy.</p>
</section>
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<ul>
<li>
<p>The KDD Cup 99 dataset is a widely used benchmark dataset in data mining and machine learning, containing 41 features and 494,021 instances. The dataset we're using is a subset of the original dataset.</p>
</li>
<li>
<p>The purpose of the KDD Cup 99 dataset is to evaluate the performance of algorithms for detecting network intrusions and other malicious activity.</p>
</li>
</ul>
</section>
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<h3>Machine Learning Models</h3>
<p>The following machine learning models were applied:</p>
<ul>
<li>Logistic Regression</li>
<li>K-Nearest Neighbors</li>
<li>Random Forest</li>
<li>Support Vector Machine</li>
</ul>
</section>
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<h2>2 - Data Exploration and Preprocessing</h2>
</section>
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<ul>
<li>The raw data is in the form of a CSV file with 25192 rows and 42 columns.</li>
</ul>
</section>
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<ul>
<li>The dataset is balanced with 13449 normal connections and 11743 malicious connections.</li>
</ul>
</section>
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<ul>
<li>dst_host_srv_serror_rate: % of connections that have "SYN" errors</li>
<li>same_srv_rate: % of connections to the same service</li>
</ul>
</section>
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<ul>
<li>Used Random Forest to determine the most important features.</li>
<li>11 features are selected based on their importance.</li>
</ul>
</section>
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<h3>Data splitting</h3>
<ul>
<li>
<p>The dataset was split into a training set (80%) and a test set (20%).</p>
</li>
<li>
<p>The test dataset is used to evaluate the final model.</p>
</li>
</ul>
</section>
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<h2>3 - Models Training</h2>
</section>
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<p>The four machine learning models that were applied are:</p>
<ul>
<li>Logistic Regression</li>
<li>K-Nearest Neighbors</li>
<li>Random Forest</li>
<li>Support Vector Machine</li>
</ul>
</section>
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<h3>Logistic Regression</h3>
<p>Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable.</p>
</section>
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<h3>K-Nearest Neighbors</h3>
<p>K Nearest Neighbors is a non-parametric method used for classification and regression.</p>
</section>
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<h3>Random Forest</h3>
<p>Random forest is a supervised learning algorithm. It can be used both for classification and regression problems.</p>
</section>
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<h3>Support Vector Machine</h3>
<p>Support Vector Machine is a supervised machine learning algorithm which can be used for both classification or regression challenges.</p>
</section>
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<h2>4 - Model Evaluation</h2>
</section>
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<ul>
<li>
<p>The confusion matrix for the Random Forest model and Logistic Regression model are equal.</p>
</li>
<li>
<p>But does that mean they'll perform the same?</p>
</li>
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<h3>Cross-Validation</h3>
<ul>
<li>Used 10-fold cross-validation to evaluate the models.</li>
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<h3>Key Insights</h3>
<ul>
<li>
<p>The Random Forest model has the best performance with an accuracy of 99.5% and a false negative rate of 0.004%.</p>
</li>
<li>
<p>Logistic Regression might be overfitting the data.</p>
</li>
<li>
<p>The Logistic Regression model has the worst performance with an accuracy of 93.7%.</p>
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<h2>5 - Summary</h2>
<ul>
<li>Machine learning models can be used to detect network intrusions and other malicious activity with high accuracy.</li>
<li>The Random Forest model has the best performance with an accuracy of 99.5%.</li>
<li>This is an alternative to the traditional signature-based detection methods.</li>
</ul>
</section>
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<h2>Thank You</h2>
<p>Full code is available on GitHub</p>
<p><a href="https://github.com/charafmrah/network-intrusion-detection">https://github.com/charafmrah/network-intrusion-detection</a></p>
</section>
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