Baseline experiments on training a Decision Tree Classifier and a Random Forest Classifier using Grid Search with Cross Validation on the CIC IDS 2018 dataset for training Machine Learning network intrusion detection classifier models.
Machine Learning Based - Intrusion Detection System
En este proyecto se evalúan y comparan diferentes técnicas de aprendizaje automático para la detección de intrusiones en red.
Machine learning based network intrusion detection system
IoT intrusion detection project enhancing detection accuracy of DoS/DDoS attacks through data imbalance correction using SMOTE and machine learning classifiers, achieving an F1-score of 0.999983.
Addressing Class Imbalance in CIC-IDS-18. Improve intrusion detection accuracy and reduce false alarms by tackling class imbalance. Utilizing artificial oversampling techniques and comparing their efficacy with deep neural network algorithms.Tech: Python, Jupyter-Notebook, Scik
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