Undergraduate Research Project - Use of Machine and Deep Learning Ensemble Techniques to Detect Computer Network Injection Attacks

Description 📋

Full article on LinkedIn.

Scientific initiation project carried out over a period of two and a half years, financed by the Foundation for Research Support of the State of São Paulo (FAPESP) [#2019/01597-7], taught by PhD Kelton Augusto Pontara da Costa from São Paulo State University (Unesp).

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Content: "Section 4 - Machine Learning and Deep Learning aimed at Network Security" 🛠️

Full article on LinkedIn.

Source code referring to "Section 4 - Machine Learning and Deep Learning aimed at Network Security" of the article referenced above:

1. Data Collection and Pre-processing;
2. Modeling, Training, Optimization, Evaluation - Part 1 - Isolated Machine Learn Techniques;
3. Modeling, Training, Optimization, Evaluation - Part 2 - Ensemble;
4. Modeling, Training, Optimization, Evaluation - Part 3 - Deep Learning Technique;
5. Communication, Results, Implementation in a Real Environment.

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Techniques and Technologies Used 🖥️

• ``Supervised Learning``
• ``Binary Classification``
• ``Support Vector Machine (SVM)``
• ``Decision Tree``
• ``Naive Bayes (Bernoulli, Gaussian, Multinomial)``
• ``Ensemble (AdaBoost, Random Forest, Gradient Boosting, Stacking)``
• ``One-Dimensional Convolutional Neural Network (1D CNN)``
• ``Synthetic Minority Over-Sampling Technique (SMOTE)``
• ``Python (urllib, csv, re, glob, time, numpy, pandas, matplotlib, mlxtend sklearn, tensorflow, keras, joblib, imblearn, google.colab)``
• ``Hypertext Transfer Protocol (HTTP/1,1) Protocol``
• ``CSIC 2010 Database``
• ``Google Colab``

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Developer 🧑‍💻

Gabriel Ferreira