Intrusion Detection in Mobile Networks Using Random Forest and CICIDS2017 Dataset

Abstract

The rapid growth of mobile networks and the increasing number of cyberattacks have created significant security challenges, making effective intrusion detection essential. To address these challenges, this project proposes a Machine Learning-Based Intrusion Detection System (IDS) for mobile networks using the CICIDS2017 dataset and the Random Forest algorithm. The CICIDS2017 dataset contains realistic network traffic, including both benign and malicious activities with different types of attacks. The proposed system preprocesses the dataset by removing missing and invalid data, handling outliers, encoding labels, and scaling the features. Recursive Feature Elimination (RFE) is applied to select the most relevant features before training the Random Forest model. The model is trained to distinguish between normal and malicious network traffic and to classify different attack types. The performance of the proposed system is evaluated using Accuracy, Precision, Recall, F1-Score, Specificity, and AUC-ROC. The results demonstrate that Random Forest can effectively detect and classify network attacks with high performance. The proposed system can therefore contribute to improving the security, reliability, and protection of mobile network environments against cyber threats.