Undergraduate Research Project - Use of Machine and Deep Learning Ensemble Techniques to Detect Computer Network Injection Attacks
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).
Full article on LinkedIn.
Source code referring to "Section 4 - Machine Learning and Deep Learning aimed at Network Security" of the article referenced above:
- Data Collection and Pre-processing;
- Modeling, Training, Optimization, Evaluation - Part 1 - Isolated Machine Learn Techniques;
- Modeling, Training, Optimization, Evaluation - Part 2 - Ensemble;
- Modeling, Training, Optimization, Evaluation - Part 3 - Deep Learning Technique;
- Communication, Results, Implementation in a Real Environment.
- ``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``
 Gabriel Ferreira |
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