Analysis of network intrusion detection using feature engineering in ML

Machine Learning

Intrusion detection Systems (IDS) play a critical role in safeguarding computer networks from malicious activities. This project aims to enhance IDS performance through the integration of feature engineering, specifically Recursive Feature Elimination (RFE), coupled with the Random Forest algorithm. Feature engineering involves selecting and transforming input variables to improve model performance. RFE, a subset selection technique, iteratively removes less significant features, enhancing model efficiency and interpretability. Random Forest, a powerful ensemble learning method, leverages decision trees to classify data, offering robustness and accuracy in complex datasets. The evaluation metrics include accuracy, precision, recall, F1-score, and Cross Validation graphs. Experimental results will demonstrate the efficiency of the proposed approach in improving IDS performance in terms of detection accuracy and efficiency. This project contributes to the field of cybersecurity by providing insights into the effectiveness of feature engineering techniques, particularly RFE, in enhancing IDS capabilities. Ultimately, this project seeks to contribute to the advancement of intrusion detection techniques, offering insights into effective feature selection strategies and algorithmic frameworks for enhancing network security in contemporary computing environments.

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